The travelers face troubles in understanding the signboards which are written in local lan- guage. The travelers can rely on smart phone for traveling purposes. Smart phones become most popular in recent years in terms of market value and the number of useful applications to the users. This work intends to build up a web application that can recognize the English content present on signboard pictures captured using a smart phone, translate the content from English to Telugu, and display the translated Telugu text back onto the screen of the phone. Experiments have been conducted on various signboard pictures and the outcomes demonstrate the viability of the proposed approach.
This document summarizes research on recognizing online handwritten Sanskrit characters using support vector classification. It discusses using Freeman chain code to extract features from character images and represent boundary pixels. A randomized algorithm generates the chain codes. Features vectors are then built and used to train a support vector machine classifier. Segmentation is also used to evaluate possible segmentation zones. The goal is to develop an accurate system for recognizing Sanskrit characters, which is challenging due to complex character shapes and styles. Previous work on character recognition is discussed, focusing on Indian scripts like Devanagari and techniques like feature extraction and classification.
FREEMAN CODE BASED ONLINE HANDWRITTEN CHARACTER RECOGNITION FOR MALAYALAM USI...acijjournal
Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm.
Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format.
Video Audio Interface for recognizing gestures of Indian sign LanguageCSCJournals
We proposed a system to robotically recognize gestures of sign language from a video stream of the signer. The developed system converts words and sentences of Indian sign language into voice and text in English. We have used the power of image processing techniques and artificial intelligence techniques to achieve the objective. To accomplish the task we used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform, image fusion techniques to segment shapes in our videos. It also uses Elliptical Fourier descriptors for shape feature extraction and principal component analysis for feature set optimization and reduction. Database of extracted features are compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into a text and voice message with 91 percent accuracy. The training and testing of the system is done using gestures from Indian Sign Language (INSL). Around 80 gestures from 10 different signers are used. The entire system was developed in a user friendly environment by creating a graphical user interface in MATLAB. The system is robust and can be trained for new gestures using GUI.
This document discusses developing an embedded speech recognition system for mobile devices. It aims to allow users to respond to text messages by voice while continuing other tasks like driving or working. The proposed system would recognize words from voice input and convert text messages to voice messages. It was developed using Visual Basic, Windows Mobile SDK, and a cellular emulator. The system intercepts messages sent to the emulator, stores them in a database, and uses text-to-speech to read the messages aloud. The document reviews related work on speech recognition and text-to-speech conversion techniques. It analyzes the design and tests the system's ability to receive, store and read out messages. Future work could expand the system to enable speech-to-text
Design of a Communication System using Sign Language aid for Differently Able...IRJET Journal
This document describes a proposed system to design a communication system using sign language to aid differently abled people. The system aims to use image processing and artificial intelligence techniques to recognize characters in sign language from video input and convert them to text and speech output. It discusses technologies like blob detection, skin color recognition and template matching that would be used for sign recognition. The system is intended to help deaf and mute people communicate by translating their sign language to a format understandable by others.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
Pre-Defense CSE Thesis Presentation in BAUSTNaiyan Noor
he Pre-Defense meeting serves as a “dress rehearsal” for the Final Defense presentation and is the opportune time to address any final edits, questions, or concerns leading up to the Final Defense.
Know the format of your thesis defence. ...
Prepare and practice your presentation. ...
The dreaded “awkward question” ...
When you don't know the answer… ...
Core content. ...
Dealing with nerves.
IRJET - Storytelling App for Children with Hearing Impairment using Natur...IRJET Journal
This document describes a storytelling app for children with hearing impairments that uses natural language processing. The app takes stories as text input and outputs the stories through Indian sign language gestures and speech. It uses algorithms like tokenization and hashmaps for text processing and translation into sign language. The design includes features like age selection, story selection, sign language output synchronized with text-to-speech, and letter-by-letter translation for words without signs. The goal is to make stories accessible and enjoyable for deaf children.
This document summarizes research on recognizing online handwritten Sanskrit characters using support vector classification. It discusses using Freeman chain code to extract features from character images and represent boundary pixels. A randomized algorithm generates the chain codes. Features vectors are then built and used to train a support vector machine classifier. Segmentation is also used to evaluate possible segmentation zones. The goal is to develop an accurate system for recognizing Sanskrit characters, which is challenging due to complex character shapes and styles. Previous work on character recognition is discussed, focusing on Indian scripts like Devanagari and techniques like feature extraction and classification.
FREEMAN CODE BASED ONLINE HANDWRITTEN CHARACTER RECOGNITION FOR MALAYALAM USI...acijjournal
Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm.
Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format.
Video Audio Interface for recognizing gestures of Indian sign LanguageCSCJournals
We proposed a system to robotically recognize gestures of sign language from a video stream of the signer. The developed system converts words and sentences of Indian sign language into voice and text in English. We have used the power of image processing techniques and artificial intelligence techniques to achieve the objective. To accomplish the task we used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform, image fusion techniques to segment shapes in our videos. It also uses Elliptical Fourier descriptors for shape feature extraction and principal component analysis for feature set optimization and reduction. Database of extracted features are compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into a text and voice message with 91 percent accuracy. The training and testing of the system is done using gestures from Indian Sign Language (INSL). Around 80 gestures from 10 different signers are used. The entire system was developed in a user friendly environment by creating a graphical user interface in MATLAB. The system is robust and can be trained for new gestures using GUI.
This document discusses developing an embedded speech recognition system for mobile devices. It aims to allow users to respond to text messages by voice while continuing other tasks like driving or working. The proposed system would recognize words from voice input and convert text messages to voice messages. It was developed using Visual Basic, Windows Mobile SDK, and a cellular emulator. The system intercepts messages sent to the emulator, stores them in a database, and uses text-to-speech to read the messages aloud. The document reviews related work on speech recognition and text-to-speech conversion techniques. It analyzes the design and tests the system's ability to receive, store and read out messages. Future work could expand the system to enable speech-to-text
Design of a Communication System using Sign Language aid for Differently Able...IRJET Journal
This document describes a proposed system to design a communication system using sign language to aid differently abled people. The system aims to use image processing and artificial intelligence techniques to recognize characters in sign language from video input and convert them to text and speech output. It discusses technologies like blob detection, skin color recognition and template matching that would be used for sign recognition. The system is intended to help deaf and mute people communicate by translating their sign language to a format understandable by others.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
Pre-Defense CSE Thesis Presentation in BAUSTNaiyan Noor
he Pre-Defense meeting serves as a “dress rehearsal” for the Final Defense presentation and is the opportune time to address any final edits, questions, or concerns leading up to the Final Defense.
Know the format of your thesis defence. ...
Prepare and practice your presentation. ...
The dreaded “awkward question” ...
When you don't know the answer… ...
Core content. ...
Dealing with nerves.
IRJET - Storytelling App for Children with Hearing Impairment using Natur...IRJET Journal
This document describes a storytelling app for children with hearing impairments that uses natural language processing. The app takes stories as text input and outputs the stories through Indian sign language gestures and speech. It uses algorithms like tokenization and hashmaps for text processing and translation into sign language. The design includes features like age selection, story selection, sign language output synchronized with text-to-speech, and letter-by-letter translation for words without signs. The goal is to make stories accessible and enjoyable for deaf children.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
This document presents a technique for identifying scripts (Tamil, English, Hindi) and numerals from multilingual document images using a rule-based classifier. Words are segmented and the first character of each word is represented as a 9-bit vector based on features like density, shape, and transitions. A rule-based classifier containing rules derived from training data is used to classify the script of each character. The technique aims to automatically categorize multilingual documents before applying optical character recognition and requires minimal preprocessing with high accuracy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The Project is based on design & implementation of smart hybrid system for street sign boards recognition, text and speech conversions through character extraction and symbol matching. The default language use to pronounce signs on the street boards is English. Here we are proposing a novel method to convert identified character or symbol into multiple languages like Hindi, Marathi, Urdu, etc. This Project is helpful to all starting from the visually impaired, the tourists, the illiterates and all the people who travel. The system is accomplished with the speech pronunciation in different languages and to display on screen. This Project has a multidisciplinary approach as it belongs to the domains like computer vision, speech processing, & Google cloud platform. Computer vision is used for character and symbol extraction from sign boards. Speech processing is used for text to speech conversion. GCP is used for multiple language conversion of original extracted text. Further programming is done for real time pronunciation and displaying desired output.
Implementation of humanoid robot with using the concept of synthetic braineSAT Journals
Abstract This paper is elaborate the model of humanoid robot interacts with human being and perform various operation as per the command given by the human being. A humanoid robot having Synthetic brain can able to do Interaction, communication, Object detection, information acquisition about any object, response to voice command, chatting logically with human beings. Object detection will be done by this robot for that purpose there is use image processing concept (HAAR Technique), And to make the system intelligent that is whenever system interact, communicate, chat with human it gives proper response, question / answers there is integrates artificial intelligence and DFA / NFA automata and Prolog language concept for answering logically over the complex and relevant strings or data. Keywords —Humanoid Robotics, Artificial Intelligence, Image Processing, Audio Filtering.
This document presents a voice-based billing system that takes voice input from customers on purchased items and quantities and generates an itemized bill. It consists of three main modules: 1) A speech-to-text module that converts voice input into text using Google APIs. 2) A word tokenization module that splits the text into individual words using NLTK. 3) A bill generation module that takes the tokenized words as input to calculate the total bill amount. The system was tested on purchasing various fruits and achieved 90% accuracy compared to existing systems. It aims to reduce time complexity for billing compared to manual entry.
This document provides an introduction to character recognition and optical character recognition (OCR). It discusses the purpose and history of OCR, including early technologies from the 1910s-1930s. It also covers the scope, technology used, and how to use OCR software. Finally, it discusses the feasibility study for an OCR project, including technical, operational, and economic feasibility. The overall purpose is to develop an efficient OCR software system to convert paper documents to electronic format for improved document processing and searchability.
This document is a B Tech project report submitted by Abhishek Agarwal in April 2005 at the Dhirubhai Ambani Institute of Information & Communication Technology. The project aims to develop machine understanding of Indian spoken languages. It discusses work done in language identification based on phonetic characteristics. The report covers background on language identification systems, objectives of the project, a discussion on developing language models for the identification process, and proposes tools that could utilize language identification algorithms.
In this file, a simple search for pattern recognition will be explained, and it contains a summary of everything you want to know, and there are some important sources and information in it.
download it from here: https://exe.io/56WbRkf
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
This document summarizes a research paper that presents an optical character recognition system for isolated Arabic characters using a DSP-based hardware implementation. The system uses a fuzzy ART neural network for character recognition after extracting features from segmented characters. Testing achieved a 95% recognition rate on 700 sample characters from various fonts and sizes. The system was implemented on a TI TMS320C6416T digital signal processor for high-speed performance with small size and low power consumption. Future work could expand the system to recognize connected characters and directly read images rather than relying on pre-processing in MATLAB.
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
This document summarizes a research paper on developing a Marathi speech recognition system to enable users to search for information on government policies and schemes in India. It describes the challenges of speech recognition including variability between speakers. The proposed system uses Mel frequency cepstral coefficients (MFCC) and Gammatone filters for feature extraction to build robust speaker-dependent and speaker-independent models. Accuracy rates are reported for the models in clean and noisy conditions. The system is implemented with a user interface to allow voice searches of text documents containing policy information stored on the local device. Evaluation of the system shows it can accurately recognize Marathi speech and retrieve relevant documents.
Text Detection and Recognition with Speech Output for Visually Challenged Per...IJERA Editor
The document reviews existing systems that aim to assist visually impaired persons by detecting and recognizing text from images and converting it to speech. It discusses how optical character recognition and text-to-speech technologies have been used to develop applications like newspaper reading systems, signage recognition systems, and camera-based text reading systems. The document also summarizes various text detection and recognition methods that have been used, such as gradient feature-based, color segmentation-based, texture feature-based, and layout analysis-based approaches.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
The document proposes a method for building a speaker independent speech recognition system that can recognize speech in any language (tested with Hindi and Bengali) and implement it on Windows 7. It discusses using adaptive language models and hidden Markov models to map acoustic signals to words irrespective of the speaker. The implementation involves developing dictionaries with words and their corresponding phonemes, designing grammars according to language structures, and using the Sphinx speech engine alongside Java code to segment words from audio input and match them to phonemes through hidden Markov modeling. The method allows for multilingual speech recognition with improved efficiency through parallelization of processing stages.
This document summarizes the technical progress in speech recognition over the past few decades. It discusses the key components of a speech recognition system including speech analysis, feature extraction, language translation, and message understanding. The document also outlines the main challenges in speech recognition like variability in context, environmental conditions, speakers, and audio quality. Furthermore, it covers different types of speech recognition systems and their applications in various sectors such as education, medicine, transportation and more. The document concludes with an overview of the major approaches to speech recognition including acoustic-phonetic, pattern recognition, and artificial intelligence and discusses popular feature extraction methods used.
Online Hand Written Character RecognitionIOSR Journals
This document discusses online handwritten character recognition. It begins by describing the differences between online and offline recognition systems. Online systems capture stroke order and timing information while writing, while offline systems analyze static images. The document then discusses challenges in recognition like variability between writers. It presents several previous works in online handwriting recognition. The document proposes a method for online recognition that uses shape, pixel density, and stroke movement template matching to identify characters. It describes preprocessing input and generating training templates to match against. Overall, the document outlines challenges in online handwriting recognition and proposes a template matching approach to address these challenges.
IRJET- Text Extraction from Text Based Image using AndroidIRJET Journal
1) The document describes a study that developed an Android application to extract text from images captured using a mobile phone camera. It uses the Tesseract OCR engine and Google Vision API to recognize text in images and display it on the screen.
2) The application aims to allow users to extract text from images for translation or reading aloud, helping those who cannot read text like images, such as non-native speakers or visually impaired people.
3) The study implemented text feature filtering, text-based retrieval algorithms and used Google APIs like Translate for translation and text-to-speech conversion to develop the application. The application performance was tested based on text extraction accuracy from images.
A SMART LANGUAGE TRANSLATION TECHNIQUE USING OCRIRJET Journal
The document summarizes a research paper on developing a smart language translation technique using optical character recognition (OCR) on Android. It discusses how traditional translation methods are difficult and time-consuming. The proposed system allows users to scan text with their phone camera, extract the text using OCR, identify the source language, and instantly translate it to another language specified by the user. It uses the Firebase ML Kit and Google Play Services for machine learning capabilities to perform text detection, recognition and translation on the device without an internet connection. The system aims to make translation more convenient and overcome issues with manual translation.
IRJET - Language Linguist using Image Processing on Intelligent Transport Sys...IRJET Journal
This document summarizes a research paper that proposes a system to detect text in images of traffic signs, extract the text, and translate it to English. The system uses convolutional neural networks (CNNs) to detect text areas and recurrent neural networks (RNNs) to translate the extracted text. The goal is to help travelers understand traffic signs written in unfamiliar languages like Spanish or French by automatically translating the text in images to English. The system performs three steps: 1) detect text areas in images of signs, 2) extract the words from the detected text regions, and 3) translate the extracted text to English for the user.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
This document presents a technique for identifying scripts (Tamil, English, Hindi) and numerals from multilingual document images using a rule-based classifier. Words are segmented and the first character of each word is represented as a 9-bit vector based on features like density, shape, and transitions. A rule-based classifier containing rules derived from training data is used to classify the script of each character. The technique aims to automatically categorize multilingual documents before applying optical character recognition and requires minimal preprocessing with high accuracy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The Project is based on design & implementation of smart hybrid system for street sign boards recognition, text and speech conversions through character extraction and symbol matching. The default language use to pronounce signs on the street boards is English. Here we are proposing a novel method to convert identified character or symbol into multiple languages like Hindi, Marathi, Urdu, etc. This Project is helpful to all starting from the visually impaired, the tourists, the illiterates and all the people who travel. The system is accomplished with the speech pronunciation in different languages and to display on screen. This Project has a multidisciplinary approach as it belongs to the domains like computer vision, speech processing, & Google cloud platform. Computer vision is used for character and symbol extraction from sign boards. Speech processing is used for text to speech conversion. GCP is used for multiple language conversion of original extracted text. Further programming is done for real time pronunciation and displaying desired output.
Implementation of humanoid robot with using the concept of synthetic braineSAT Journals
Abstract This paper is elaborate the model of humanoid robot interacts with human being and perform various operation as per the command given by the human being. A humanoid robot having Synthetic brain can able to do Interaction, communication, Object detection, information acquisition about any object, response to voice command, chatting logically with human beings. Object detection will be done by this robot for that purpose there is use image processing concept (HAAR Technique), And to make the system intelligent that is whenever system interact, communicate, chat with human it gives proper response, question / answers there is integrates artificial intelligence and DFA / NFA automata and Prolog language concept for answering logically over the complex and relevant strings or data. Keywords —Humanoid Robotics, Artificial Intelligence, Image Processing, Audio Filtering.
This document presents a voice-based billing system that takes voice input from customers on purchased items and quantities and generates an itemized bill. It consists of three main modules: 1) A speech-to-text module that converts voice input into text using Google APIs. 2) A word tokenization module that splits the text into individual words using NLTK. 3) A bill generation module that takes the tokenized words as input to calculate the total bill amount. The system was tested on purchasing various fruits and achieved 90% accuracy compared to existing systems. It aims to reduce time complexity for billing compared to manual entry.
This document provides an introduction to character recognition and optical character recognition (OCR). It discusses the purpose and history of OCR, including early technologies from the 1910s-1930s. It also covers the scope, technology used, and how to use OCR software. Finally, it discusses the feasibility study for an OCR project, including technical, operational, and economic feasibility. The overall purpose is to develop an efficient OCR software system to convert paper documents to electronic format for improved document processing and searchability.
This document is a B Tech project report submitted by Abhishek Agarwal in April 2005 at the Dhirubhai Ambani Institute of Information & Communication Technology. The project aims to develop machine understanding of Indian spoken languages. It discusses work done in language identification based on phonetic characteristics. The report covers background on language identification systems, objectives of the project, a discussion on developing language models for the identification process, and proposes tools that could utilize language identification algorithms.
In this file, a simple search for pattern recognition will be explained, and it contains a summary of everything you want to know, and there are some important sources and information in it.
download it from here: https://exe.io/56WbRkf
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
This document summarizes a research paper that presents an optical character recognition system for isolated Arabic characters using a DSP-based hardware implementation. The system uses a fuzzy ART neural network for character recognition after extracting features from segmented characters. Testing achieved a 95% recognition rate on 700 sample characters from various fonts and sizes. The system was implemented on a TI TMS320C6416T digital signal processor for high-speed performance with small size and low power consumption. Future work could expand the system to recognize connected characters and directly read images rather than relying on pre-processing in MATLAB.
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
This document summarizes a research paper on developing a Marathi speech recognition system to enable users to search for information on government policies and schemes in India. It describes the challenges of speech recognition including variability between speakers. The proposed system uses Mel frequency cepstral coefficients (MFCC) and Gammatone filters for feature extraction to build robust speaker-dependent and speaker-independent models. Accuracy rates are reported for the models in clean and noisy conditions. The system is implemented with a user interface to allow voice searches of text documents containing policy information stored on the local device. Evaluation of the system shows it can accurately recognize Marathi speech and retrieve relevant documents.
Text Detection and Recognition with Speech Output for Visually Challenged Per...IJERA Editor
The document reviews existing systems that aim to assist visually impaired persons by detecting and recognizing text from images and converting it to speech. It discusses how optical character recognition and text-to-speech technologies have been used to develop applications like newspaper reading systems, signage recognition systems, and camera-based text reading systems. The document also summarizes various text detection and recognition methods that have been used, such as gradient feature-based, color segmentation-based, texture feature-based, and layout analysis-based approaches.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
The document proposes a method for building a speaker independent speech recognition system that can recognize speech in any language (tested with Hindi and Bengali) and implement it on Windows 7. It discusses using adaptive language models and hidden Markov models to map acoustic signals to words irrespective of the speaker. The implementation involves developing dictionaries with words and their corresponding phonemes, designing grammars according to language structures, and using the Sphinx speech engine alongside Java code to segment words from audio input and match them to phonemes through hidden Markov modeling. The method allows for multilingual speech recognition with improved efficiency through parallelization of processing stages.
This document summarizes the technical progress in speech recognition over the past few decades. It discusses the key components of a speech recognition system including speech analysis, feature extraction, language translation, and message understanding. The document also outlines the main challenges in speech recognition like variability in context, environmental conditions, speakers, and audio quality. Furthermore, it covers different types of speech recognition systems and their applications in various sectors such as education, medicine, transportation and more. The document concludes with an overview of the major approaches to speech recognition including acoustic-phonetic, pattern recognition, and artificial intelligence and discusses popular feature extraction methods used.
Online Hand Written Character RecognitionIOSR Journals
This document discusses online handwritten character recognition. It begins by describing the differences between online and offline recognition systems. Online systems capture stroke order and timing information while writing, while offline systems analyze static images. The document then discusses challenges in recognition like variability between writers. It presents several previous works in online handwriting recognition. The document proposes a method for online recognition that uses shape, pixel density, and stroke movement template matching to identify characters. It describes preprocessing input and generating training templates to match against. Overall, the document outlines challenges in online handwriting recognition and proposes a template matching approach to address these challenges.
IRJET- Text Extraction from Text Based Image using AndroidIRJET Journal
1) The document describes a study that developed an Android application to extract text from images captured using a mobile phone camera. It uses the Tesseract OCR engine and Google Vision API to recognize text in images and display it on the screen.
2) The application aims to allow users to extract text from images for translation or reading aloud, helping those who cannot read text like images, such as non-native speakers or visually impaired people.
3) The study implemented text feature filtering, text-based retrieval algorithms and used Google APIs like Translate for translation and text-to-speech conversion to develop the application. The application performance was tested based on text extraction accuracy from images.
A SMART LANGUAGE TRANSLATION TECHNIQUE USING OCRIRJET Journal
The document summarizes a research paper on developing a smart language translation technique using optical character recognition (OCR) on Android. It discusses how traditional translation methods are difficult and time-consuming. The proposed system allows users to scan text with their phone camera, extract the text using OCR, identify the source language, and instantly translate it to another language specified by the user. It uses the Firebase ML Kit and Google Play Services for machine learning capabilities to perform text detection, recognition and translation on the device without an internet connection. The system aims to make translation more convenient and overcome issues with manual translation.
IRJET - Language Linguist using Image Processing on Intelligent Transport Sys...IRJET Journal
This document summarizes a research paper that proposes a system to detect text in images of traffic signs, extract the text, and translate it to English. The system uses convolutional neural networks (CNNs) to detect text areas and recurrent neural networks (RNNs) to translate the extracted text. The goal is to help travelers understand traffic signs written in unfamiliar languages like Spanish or French by automatically translating the text in images to English. The system performs three steps: 1) detect text areas in images of signs, 2) extract the words from the detected text regions, and 3) translate the extracted text to English for the user.
This document summarizes a student project on OCR-based image text-to-speech conversion. It introduces the topic, describes using OCR to extract text from images which is then converted to speech. It includes an abstract, details of related works, and concludes that the proposed system allows visually impaired users to hear text extracted from images efficiently using Tesseract OCR and text-to-speech conversion.
INDIAN SIGN LANGUAGE TRANSLATION FOR HARD-OF-HEARING AND HARD-OF-SPEAKING COM...IRJET Journal
This document proposes a system to translate Indian sign language to text and speech in real-time using machine learning. It aims to help communication between the hard-of-hearing and hard-of-speaking communities by eliminating the need for interpreters. The system would use a webcam or phone camera to capture sign language gestures which are then preprocessed and fed into a convolutional neural network model to recognize the signs and output the translation as text. Previous studies that used other methods like CNNs, RNNs, and HMMs to translate sign language are summarized along with their accuracies ranging from 91.5% to 99%. The proposed system architecture involves capturing gestures, preprocessing, detecting the sign, and displaying the translation for sign to text
The document discusses a new approach for identifying the script of words in low-resolution images of display boards using texture features. It aims to identify 3 Indian scripts: Hindi, Kannada, and English. The proposed method extracts discrete cosine transform-based texture features from word images and uses a threshold-based function to classify the script. When evaluated on 800 word images, it achieved an overall accuracy of 85.44% and individual accuracies of 100% for Hindi, 70.33% for Kannada, and 86% for English. The method is robust to variations in fonts, character spacing, noise and other degradations.
IRJET - Number/Text Translate from ImageIRJET Journal
This document discusses a text/number translation system using machine learning and Firebase ML Kit. It describes how the system works, including scanning text with a mobile device camera, extracting the text using recognition APIs, and translating the text. The system aims to help travelers and visually impaired users access important text written in unfamiliar languages. It outlines the methodology used for text detection, recognition, and language identification. Potential applications are discussed, such as assisting the blind or helping people learn new languages. Future work could involve adding speech output to help visually impaired users with language learning disabilities.
Audio computing Image to Text Synthesizer - A Cutting-Edge Content Generator ...IRJET Journal
The document describes an image to text synthesizer application that can extract text from images using optical character recognition (OCR) and convert it to audio, different languages, or handwritten notes. It uses technologies like Tesseract for OCR, Firebase for cloud storage, Google Translate for translations, and gTTS and PyWhatKit for text-to-speech and handwriting conversion. The application aims to help visually impaired people access text and illiterate people understand different languages by converting images to editable, translated and audio forms of text. It achieved over 85% accuracy on a test set of 100 images.
Handwritten Text Recognition and Translation with AudioIRJET Journal
This document presents research on handwritten text recognition and translation with audio. The researchers used convolutional neural networks to classify handwritten words and characters. For word classification, they directly classified whole words. For character classification, they first separated characters from words using an LSTM model and then classified each character. They trained models on the IAM Handwriting Dataset containing over 100k words. Pre-processing steps like noise removal, skew correction, and line segmentation were used to prepare images for classification. Both word-level and character-level classification models were explored. The character classification approach showed better results due to a smaller output size for the softmax layer. The recognized text could then be translated and output with audio.
IRJET- ASL Language Translation using MLIRJET Journal
This document presents a survey of technologies for hand sign language recognition and translation to text using machine learning. It discusses using CNN models to identify hand gestures in real-time from video input and translate the gestures to words rather than individual letters for better communication between deaf and hearing people. The system architecture involves hand detection, gesture recognition using a CNN model, and a login system for users. Previous approaches discussed include using sequential pattern mining and hidden Markov models on extracted motion features from video frames. The goal is to build an effective communication medium between deaf and hearing individuals.
IMAGE TO TEXT TO SPEECH CONVERSION USING MACHINE LEARNINGIRJET Journal
This document describes a study on converting images to text to speech using machine learning. The researchers developed a system that uses optical character recognition to extract text from images, then converts the text to speech. They achieved over 99% accuracy on their test dataset of over 1 million images. Their integrated system was able to accurately extract and convert text from various real-world images like street signs and menus. The system has potential to improve accessibility for people with visual impairments by allowing printed information to be converted to audio. Future work includes handling lower quality images and expanding the system to support additional languages and applications.
Product Label Reading System for visually challenged peopleIRJET Journal
1) The document proposes a camera-based assistive text reading system to help blind people read text labels on handheld objects. It uses computer vision techniques like stroke width transform to isolate the object of interest and detect the region of interest.
2) In the region of interest, the system performs text localization using gradient features and edge distributions. It then recognizes text using optical character recognition and outputs it verbally for the user.
3) The system aims to achieve robust text extraction and recognition from complex backgrounds while focusing on usability. It analyzes existing assistive technologies and proposes an improved workflow including image capture, processing, and audio output.
IRJET- Sign Language Interpreter using Image Processing and Machine LearningIRJET Journal
This document describes a system to translate sign language gestures to text or audio using image processing and machine learning techniques. The system takes an image of a sign language gesture as input using a webcam. It then performs preprocessing steps like skin detection and edge detection. Features are extracted from the preprocessed image using a Histogram of Oriented Gradients algorithm. These features are fed into a Support Vector Machine classifier that has been trained on a dataset of 6000 images of English alphabet signs. The system is able to recognize the signs with 88% accuracy and translate them to text or audio output, aiding communication for deaf individuals.
Speech recognition is an advanced technology that uses desired equipment and a service which can be controlled through voice without touching the screen of the smart phone. In current century, there are many researches with the help of speech recognition on mobile devices. In this system, mobile phone users can command with their voice to easily make phone call. Google's cloud speech API is used to recognize the incoming user voice. The speech API recognizes over 120 languages but it cannot correctly provide Myanmar Language still now. The system will classify the Myanmar proper name recognized by the Googles speech API to get the correct name with the help of Naïve Bayesian Classifier. The contact name classified by Naive Bayes can only meet user's desired one just written in English script and it cannot provide the name written in Myanmar script. This system uses hybrid transliteration approach to solve the contact name recorded by Myanmar script. Therefore the system can make phone call to the contact name typed with not only English script but also Myanmar script. The system applies Jaro Winker distance measure to outperform the accuracy of system output. Success rate is used to measure the performance of each process contained in the system. This system is implemented with Android programming language. Aye Thida | Yee Wai Khaing "Voice Command Mobile Phone Dialer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26814.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26814/voice-command-mobile-phone-dialer/aye-thida
IRJET - Sign Language Text to Speech Converter using Image Processing and...IRJET Journal
This document describes a sign language text-to-speech converter system using image processing and convolutional neural networks (CNNs). The system captures images of hand gestures using a camera, applies image processing techniques like thresholding and blurring, and then uses a CNN model trained on a dataset of gestures to recognize the gestures and convert them to text and speech. The system was able to accurately recognize gestures for letters and numbers with about 85% accuracy. Future work may involve expanding the dataset to include more signs and working towards word and sentence recognition.
Instant speech translation has potential applications in reducing language barriers and costs for global businesses, as well as in consumer applications on mobile devices. It requires high accuracy speech recognition, language parsing, and machine translation. Major tech companies like Google, Microsoft, and IBM are developing prototypes, with applications expected in mobile phones and devices in the near future. Challenges remain in achieving human-like real-time performance across various environments and capturing nuances of speech. Widespread adoption of instant speech translation could significantly change how people communicate and access information globally.
IDE Code Compiler for the physically challenged (Deaf, Blind & Mute)IRJET Journal
This document describes a proposed IDE code compiler that would allow programming through speech recognition or sign language recognition, making programming more accessible for the deaf, blind, and mute communities. The proposed compiler would give users a choice of textual, audio, or gesture-based input. It would use machine learning and convolutional neural networks to recognize speech or sign language gestures and convert them to text that could be compiled in the IDE. The goal is to enable programming without typing for those with physical disabilities.
Text region extraction from low resolution display board imaIAEME Publication
The document presents a new method for extracting text regions from low resolution display board images using wavelet features. The method divides the input image into 50x50 pixel blocks and extracts wavelet energy features from each block at two resolution levels. These features are used to classify blocks as text or non-text using discriminant functions. Detected text blocks are then merged to extract text regions. The method achieved a 97% detection rate on a variety of 100 low resolution display board images each sized 240x320 pixels.
A Translation Device for the Vision Based Sign Languageijsrd.com
The Sign language is very important for people who have hearing and speaking deficiency generally called Deaf and Mute. It is the only mode of communication for such people to convey their messages and it becomes very important for people to understand their language. This paper proposes the method or algorithm for an application which would help in recognizing the different signs which is called Indian Sign Language. The images are of the palm side of right and left hand and are loaded at runtime. The method has been developed with respect to single user. The real time images will be captured first and then stored in directory and on recently captured image and feature extraction will take place to identify which sign has been articulated by the user through SIFT(scale invariance Fourier transform) algorithm. The comparisons will be performed in arrears and then after comparison the result will be produced in accordance through matched key points from the input image to the image stored for a specific letter already in the directory or the database the outputs for the following can be seen in below sections. There are 26 signs in Indian Sign Language corresponding to each alphabet out which the proposed algorithm provided with 95% accurate results for 9 alphabets with their images captured at every possible angle and distance.
IRJET- Vision Based Sign Language by using MatlabIRJET Journal
This document discusses vision-based sign language translation using MATLAB. It describes a system that uses a camera to capture images of hand gestures representing letters or words in sign language. MATLAB is used to analyze the images, recognize the gestures, and translate them into spoken words that are output through a speaker. The system aims to help deaf, mute, and blind individuals communicate more easily. Several image processing and machine learning techniques for hand segmentation, feature extraction, and classification are reviewed from previous studies. The results suggest this type of system could accurately translate sign language in real-time.
Similar to Signboard Text Translator: A Guide to Tourist (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
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Signboard Text Translator: A Guide to Tourist
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2496 – 2501
ISSN: 2088-8708 2496
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
Signboard Text Translator: A Guide to Tourist
Ilaiah Kavati, G Kiran Kumar, Sarika Kesagani and K Srinivasa Rao
Dept. of CSE, MLR Institute of Technology, Hyderabad-43, India
Article Info
Article history:
Received: Mar 11, 2017
Revised: Jun 5, 2017
Accepted: Jun 20, 2017
Keyword:
Signboard
OCR
Tesseract
Translator
Web Application
ABSTRACT
The travelers face troubles in understanding the signboards which are written in local lan-
guage. The travelers can rely on smart phone for traveling purposes. Smart phones become
most popular in recent years in terms of market value and the number of useful applications
to the users. This work intends to build up a web application that can recognize the English
content present on signboard pictures captured using a smart phone, translate the content
from English to Telugu, and display the translated Telugu text back onto the screen of the
phone. Experiments have been conducted on various signboard pictures and the outcomes
demonstrate the viability of the proposed approach.
Copyright c 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ilaiah Kavati
MLR Institute of Technology
Dundigal, Hyderabad - 43
+91 - 9848916272
kavati089@gmail.com
1. INTRODUCTION
We live in a society where we speak with individuals and data frameworks through differing media. Large
volumes of data is represented in natural scenes. Signs are all around in our lives. A sign is an article that proposes
the nearness of an actuality. It can be a shown structure bearing letters or symbols, used to recognize something or
publicize a business. It can also be a posted notification bearing a warning, safety advisory, or command, etc. Signs
are great examples in regular habitats that have high data content. They make our lives simpler when we can read
and follow them, but they posture issues or much threat when we are most certainly not. For instance, a traveler
won’t have the capacity to understand a sign in local language that indicates notices or risks [1]. In this work, we
concentrate on recognizing text on signs. Sign board text translator framework uses a smart phone to capture the
signboard image containing signs, recognizes it, and translates it into user specified language.
Automatic detection and recognition of content from common scenes are requirements for a signboard text
translator. The main challenge lies in the assortment of text: it can vary in text style, size, font, and orientation. There
may also blur in the text and can be blocked by other objects in the signboard. As signs exist in three-dimensional
space, content on signs can be misshaped by inclination, tilt, and shape of objects on which they are found. A
numerous OCR frameworks function very well on high quality images; however, they perform poor for signboard
images because of low quality nature of them. The proposed approach uses a Tesseract OCR engine to extract the
text from the acquired image [2]. We have successfully applied the proposed approach to a English-Telugu sign text
translation framework, which can recognize English signs caught from a camera, and translate the recognized text
into Telugu. To our knowledge, English to Telugu signboard translator has not been investigated previously.
Rest of the paper is organized as follows: The present advancements on text translators will be explored in
Section II. Section III explores the proposed approach. The outcomes are discussed in Section IV. Finally, Section V
concludes our work.
2. RELATED WORK
This section explores about current signboard text translator applications developed for different languages
using Optical Character Recognition (OCR). The work is accomplished for English to Chinese, Japanese to English,
Journal Homepage: http://iaesjournal.com/online/index.php/IJECE
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
, DOI: 10.11591/ijece.v7i5.pp2496-2501
2. IJECE ISSN: 2088-8708 2497
Figure 1. Block Diagram of the proposed approach
Chinese to English, Hindi to Tamil, English to Spanish, Malay to English/Arabic and Hindi to English on various
versatile stages. Following are some articles exist in the literature for text translation in different languages.
Ma et al. built up an Android-based text translation application that can perceive the content caught by a
cellular telephone camera, translate the content, and show the interpretation result back onto the screen of the cell
telephone. This application empowers the clients to get text translation as simplicity as a button click. The camera
catches the content and returns the interpreted result continuously. This framework contains automatic text detection,
optical character recognition, text correction, and text translation [3].
Watanabe et al. report an application which interprets Japanese writings in a scene into English. The
application is intended to keep running on a cell phone with a camera. It recognizes Japanese characters detected by
an installed camera of a cellular phone and translate them into English [4]. In another approach, Chen et al. presents
an approach to deal with automatic recognition of signs from natural scenes, and its application to a signboard
translator. The proposed approach uses multiresolution and multiscale edge detection, color analysis in a hierarchy
for sign identification. The strategy can essentially enhance text detection rate and OCR exactness. Rather than
utilizing binary data for OCR, they retrieve features from the picture straightforwardly. They use a local intensity
normalization technique to adequately handle lighting variations, trailed by a Gabor transform to get local features
and lastly a Linear Discriminate Analysis (LDA) strategy for feature selection. They applied this methodology in
developing a Chinese sign translation framework, which can consequently recognize Chinese signs taken from a
camera, and translate the text into English [5].
In another approach, Authors proposed a design for English to Spanish translation of the signs present on
JPEG pictures taken with a cellular phone camera. They distinguish the text using the frequency data of the DCT
coefficients, binarize it utilizing a clustering algorithm, recognize it by the utilization of an OCR algorithm and
phonetically translate it from English to Spanish. The outcome is a helpful, basic, moderate and robust framework
that can deal with issues like lighting variations, focusing, low resolution and etc, in a short time [1]. Muthalib et
al. explores a framework for text translation. It uses mobile technology and the framework is examined in which it
included 30 global undergraduates of Malaysia. Quickly this study found that clients accept the Mobile-Translator
well. The mobile translator particularly for use in Malaysia, in which the local language is Malay that can be
translated into either English or Arabic [6].
Mishra and Patvardhan presents a greatly on-interest, quick and easy to understand Android Application
ATMA. ATMA stands for Android Travel Mate Application. This application is helpful for local Tourists and Trav-
elers who have Android Smart telephones. It empowers Travelers and Tourists to effectively catch the local nation
language Books pages, signboards, flags and lodging menus and so on. The implicit OCR changes over the text
translator in the caught image into Unicode content arrangement. It likewise gives interpretation office with the goal
that Tourists can translate Native Language Unicode content into their own particular nation language. This Appli-
cation has advanced features such as copy paste, share and search for travel related inquiries like museums, spots,
hotels, books, restaurants and so forth [7].
3. PROPOSED METHOD
The proposed system capture the signboard images through a Camera phone. The captured image is then
submitted to a central server. The image is preprocessed to extract the text and is reccognized. The recognized text
is then converted to user specified language and delivered appropriately. The proposed approach is shown in Figure
1. The proposed approach follows these steps: (i) Sign board image acquisition, (ii) Extraction and recognition of
text from the captured image, (iii) Translation of the extracted text into user specified language.
3.1. Sign board image acquisition
We use a mobile camera to capture the signboard image. We restrict the distance of the camera from the
signboard should be a maximum of 10 meters to get good quality images. As the images are captured using a
mobile camera, they may be generally noisy and contains complex backgrounds. Hence, the textual image should be
pre-processed to make it clear so that the system can easily extract the text and recognizes it.
Signboard Text Translator: A Guide to Tourist (Ilaiah Kavati)
3. 2498 ISSN: 2088-8708
Figure 2. Optical Character Recognition
3.2. Text Extraction and Recognition
Extraction and recognition of text from the captured image is called Optical Character Recognition (OCR).
In this paper, we use a Tesseract OCR engine to extract the text from the acquired image [2]. The Tesseract is an open-
source OCR engine that was developed at HP between 1984 and 1994. The Optical Character Recognition (OCR)
process is divided into the following phases: Preprocessing [8], Segmentation, Feature Extraction and classification.
Figure 2 shows the phases of OCR.
3.2.1. Preprocessing
The pre-processing involves: Image contrast enhancement [9], Noise removal, Binarization and smooth-
ing. To enhance the contrast, the images are subjected to adaptive histogram equalization. The adaptive histogram
equalization increases the contrast of the image and reduces the possible imperfections in the image. The enhanced
images are further processed with median filter to suppress the noise content. The resulting image is subjected to lo-
cally adaptive threshold method to obtain a binary image. Finally smoothing which implies both filling and thinning.
Filling eliminates small breaks, gaps and holes in the digitized characters. Thinning reduces the width of the line.
The normalization is applied to obtain characters of uniform size, slant and rotation.
3.2.2. Segmentation
Segmentation is a process which helps to divide each character from a word present in a given image/page.
The objective of the segmentation is to extract each character from the text present in the thinned image [10]. After
performing segmentation, the characters of the string will be separated and it will be used for further processing. A
horizontal projection profile technique [11] is used to segment the text area from the thinned image. This technique
scans the imput image horizontally to find the first and last black pixels in a line. Once these pixels are found, the
area in between these pixels represents the line that may contain one or more character.
3.2.3. Feature Extraction and Classification
In this stage, the features of the characters that are crucial for classifying them at recognition stage are
extracted. The classification is the process of identifying each character and assigning to it the correct character
class. The classification approaches are two types. 1) Decision-theoretic methods: The principal approaches to
decision-theoretic recognition are minimum distance classifiers, statistical classifiers and neural networks. 2) Struc-
tural Methods: Measures of similarity based on relationships between structural components may be formulated by
using grammatical concepts. Suppose that we have two different character classes which can be generated by the
two grammars G1 and G2, respectively. Given an unknown character, we say that it is more similar to the first class
if it may be generated by the grammar G1, but not by G2.
3.2.4. Postprocessing
This step performs grouping of symbols and errors handling. The process of performing the association
of symbols into strings is commonly referred to as grouping. It is based on the symbols location in the document.
Symbols that are found to be sufficiently close are grouped together.
3.3. Text Translator
This section explains the process of translating the extracted text from the signboard image into user speci-
fied language. To do this, we propose a translator module that maintain a dictionary of warnings or hazards in English
(such as Poison, Do Not Drink, No Smoking, Danger, No Entry, Flammable Area, etc. which are generally appear on
signboards) along with their meaning in user specified language i.e., Telugu. Whenever the system recognizes a text
IJECE Vol. 7, No. 5, October 2017: 2496 – 2501
4. IJECE ISSN: 2088-8708 2499
(a) (b)
Figure 3. (a) Sample signboard image in English, (b) Translated text from English to Telugu
in English language, it will verify the dictionary and the corresponding Telugu text is retrieved which is displayed
over the cell phone display. This approach can be easily adopted to other launguages by feeding the required words
and their meaning in the required language.
4. RESULTS
This section explain the results of our work. To measure the performance of the proposed system, we used
two measures namely: Precision and Accuracy.
• Precision: Precision is also called character recognition rate. Precision is the number of characters correctly
recognized to the total number of characters tested. This is given in Equation 1, where nc is the number of
correctly recognized characters and Nc is total number of characters.
Precision =
nc
Nc
(1)
• Accuracy: Accuracy is the number of signboard images correctly recognized to the total number of signboard
images tested. This is given in Equation 2, where nw is the number of correctly recognized characters and Nw
is total number of characters.
Accuracy =
nw
Nw
(2)
We experimented our approach on 20 signboard images. Samples of some signboard images are shown in
Figure 3, Figure 4 and Figure 5. The proposed system achieves a precision of 93.6 %. In other words, our system
is able to extract the characters from the signboards and recognizes them effectively. Further, the accuracy of the
system depends on the character recognition rate i.e., Precision. As the system shows an acceptable level of character
recognition, our system achieves enhanced accuracy. We experimented our approach on 20 signboard images and for
18 instances they are correctly translated into the specified Telugu language. In other words, the system achieves an
accuracy of 90%. The sample signboard images taken and the obtained results are shown in Figure 3, Figure 4 and
Figure 5. We also experimented this approach for the text with different fonts and the system recognizes/ translated
them correctly.
5. CONCLUSION
This system is developed to make it easier for tourists from the states of Telangana and Andhrapradesh to
translate the signboards written in English which could be diffficult for them to understand during their trips. We
proposed a system to translate signboard images captured using a mobile phone from English to Telugu. The system
is able to translate the text in different color, light, text on dark background, image with blurring, etc. The system
Signboard Text Translator: A Guide to Tourist (Ilaiah Kavati)
5. 2500 ISSN: 2088-8708
(a) (b)
Figure 4. (a) Sample signboard image in English, (b) Translated text from English to Telugu
(a) (b)
Figure 5. (a) Sample signboard image in English, (b) Translated text from English to Telugu
achieved a Precision i.e., character recognition rate of 93.6% and translation accuracy of 90%. This shows the effi-
ciency of the Tessaract OCR engine for text extraction and recognition. Our system shows some characteristics that
make it interesting and deserve for further research. Future work involves: 1. automatic recognition and translation
of handwritten English signboards into Telugu that involves language processors; 2. Usage of more accurate OCR
for increased performance; 3. Developing a Mobile application.
REFERENCES
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[4] Y. Watanabe, K. Sono, K. Yokomizo, and Y. Okada, “Translation camera on mobile phone.” in icme, vol. 3,
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[5] X. Chen, J. Yang, J. Zhang, and A. Waibel, “Automatic detection and recognition of signs from natural scenes,”
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[6] A. A. Muthalib, A. Abdelsatar, M. Salameh, and J. Dalle, “Making learning ubiquitous with mobile translator
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BIOGRAPHIES OF AUTHORS
Dr Ilaiah Kavati is a Professor of Computer Science and Engineering Department at MLR Institute
of Technology, Hyderabad, India. He obtained his Ph.D. in Computer Science from the University
of Hyderabad (Central University), India in 2016. He recieved his B.Tech and M.Tech degrees
from Jawaharlal Nehru Technological University, Hyderabad in 2004 and 2010 respectively. He
is having 13 years of teaching and research experience. His current researches are in the fields
of image processing, biometrics, security, internet of things and big data analytics. He is a life
member of ISTE professsional society. He published more than twenty publications in international
conferences and journals.
Dr G Kiran Kumar is a Professor of Computer Science and Engineering Department at MLR Insti-
tute of Technology, Hyderabad, India. He obtained his Ph.D. in Computer Science & Engineering
from Nagarjuna University, Andhra Pradesh, India in 2015. He recieved his B.Tech and M.Tech
degrees from Osmania University, Hyderabad. He is having more than 15 years of teaching and
research experience. He is a life member of ISTE. His current researches are in the fields of Spatial
Data Mining, image processing, big data and cloud computing.
Sarika Kesagani is an Assistant Professor of Computer Science and Engineering Department at
MLR Institute of Technology, Hyderabad, India. She recieved her B.Tech and M.Tech degrees
from Jawaharlal Nehru Technological University, Hyderabad in 2012 and 2016 respectively. Her
current researches are in the fields of image processing and Internet of Things.
Dr K Srinivas Rao is the Professor of Computer Science and Engineering Department at MLR In-
stitute of Technology, Hyderabad, India. He obtained his Ph.D. in Computer Science & Engineering
from Anna University, Tamilnadu, India. He recieved his B.Tech and M.Tech degrees from Osma-
nia University, Hyderabad. He is having more than 20 years of teaching and research experience.
His current researches are in the fields of Data Mining, image processing and big data analytics.
Signboard Text Translator: A Guide to Tourist (Ilaiah Kavati)