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.
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
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.
Character Recognition System for Modi Scriptijceronline
Optical character recognition (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-generated text. Character recognition is one of the oldest fields of research since the advent of computers. Till now OCR has been developed for several languages like English, Devnagari (Hindi), Bangla etc. But not much work has been done for Modi script. Modi script has been used in Maharashtra in the 17th century, Modi script was widely used in Maharashtra for writing up to 1950 and contained lot of literature of various philosophers and has its own historical importance. And to convert that literature into computer readable format using optical character recognition systems is our objective. Hence the proposed system will be beneficial for extracting the literature from Modi script. This project work elaborates algorithm required for Modi script OCR. After initial stage of Segmentation, we have used Affine Moment Invariants for calculating moments of each character. These moments were used for training of the system and building database. Thereafter classification has been performed using Fuzzy Logic.
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
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.
Character Recognition System for Modi Scriptijceronline
Optical character recognition (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-generated text. Character recognition is one of the oldest fields of research since the advent of computers. Till now OCR has been developed for several languages like English, Devnagari (Hindi), Bangla etc. But not much work has been done for Modi script. Modi script has been used in Maharashtra in the 17th century, Modi script was widely used in Maharashtra for writing up to 1950 and contained lot of literature of various philosophers and has its own historical importance. And to convert that literature into computer readable format using optical character recognition systems is our objective. Hence the proposed system will be beneficial for extracting the literature from Modi script. This project work elaborates algorithm required for Modi script OCR. After initial stage of Segmentation, we have used Affine Moment Invariants for calculating moments of each character. These moments were used for training of the system and building database. Thereafter classification has been performed using Fuzzy Logic.
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.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
Identification of scripts from multi-script document is one of the important steps in the design
of an OCR system for successful analysis and recognition. Most optical character recognition
(OCR) systems can recognize at most a few scripts. But for large archives of document images
containing different scripts, there must be some way to automatically categorize these
documents before applying the proper OCR on them. Much work has already been reported in
this area. In the Indian context, though some results have been reported, the task is still at its
infancy. This paper presents a research in the identification of Tamil, English and Hindi
scripts at word level irrespective of their font faces and sizes. It also identifies English
numerals from multilingual document images. The proposed technique performs document
vectorization method which generates vectors from the nine zones segmented over the
characters based on their shape, density and transition features. Script is then determined by
using Rule based classifiers and its sub classifiers containing set of classification rules which
are raised from the vectors. The proposed system identifies scripts from document images
even if it suffers from noise and other kinds of distortions. Results from experiments,
simulations, and human vision encounter that the proposed technique identifies scripts and numerals with minimal pre-processing and high accuracy. In future, this can also be extended for other scripts.
Optical Character Recognition System for Urdu (Naskh Font)Using Pattern Match...CSCJournals
The offline optical character recognition (OCR) for different languages has been developed over the recent years. Since 1965, the US postal service has been using this system for automating their services. The range of the applications under this area is increasing day by day, due to its utility in almost major areas of government as well as private sector. This technique has been very useful in making paper free environment in many major organizations as far as the backup of their previous file record is concerned. Our this system has been proposed for the Offline Character Recognition for Isolated Characters of Urdu language, as Urdu language forms words by combining Isolated Characters. Urdu is a cursive language, having connected characters making words. The major area of utility for Urdu OCR will be digitizing of a lot of literature related material already stocked in libraries. Urdu language is famous and spoken in more than 3 big countries including Pakistan, India and Bangladesh. A lot of work has been done in Urdu poetry and literature up to the recent century. Creation of OCR for Urdu language will make an important role in converting all those work from physical libraries to electronic libraries. Most of the stuff already placed on internet is in the form of images having text, which took a lot of space to transfer and even read online. So the need of an Urdu OCR is a must. The system is of training system type. It consists of the image preprocessing, line and character segmentation, creation of xml file for training purpose. While Recognition system includes taking xml file, the image to be recognized, segment it and creation of chain codes for character images and matching with already stored in xml file. The system has been implemented and it has 89% recognition accuracy with a 15 char/sec recognition rate.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
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.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
The Presentation is about Optical Character Recognition, Talks about high technology devices such as Bar-code scanner, book readers, Image to Tech Converter and paper Scanners
An Efficient Segmentation Technique for Machine Printed Devanagiri Script: Bo...iosrjce
Segmentation technique plays a major role in scripting the documents for extraction of various
features. Many researchers are doing various research works in this field to make the segmenting process
simple as well as efficient. In this paper a simple segmentation technique for both the line and word
segmentation of a script document has been proposed. The main objective of this technique is to recognize the
spaces that separate two text lines.For the Word segmentation technique also similar procedure is followed. In
this work ,three different scanned document have been taken as input images for both line and word
segmentation techniques. The results found were outstanding with average accuracy for both line and word. It
provides 100% accuracy for line segmentation and 100% for line segmentation as well. Evaluation results show
that our method outperforms several competing methods.
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.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
Identification of scripts from multi-script document is one of the important steps in the design
of an OCR system for successful analysis and recognition. Most optical character recognition
(OCR) systems can recognize at most a few scripts. But for large archives of document images
containing different scripts, there must be some way to automatically categorize these
documents before applying the proper OCR on them. Much work has already been reported in
this area. In the Indian context, though some results have been reported, the task is still at its
infancy. This paper presents a research in the identification of Tamil, English and Hindi
scripts at word level irrespective of their font faces and sizes. It also identifies English
numerals from multilingual document images. The proposed technique performs document
vectorization method which generates vectors from the nine zones segmented over the
characters based on their shape, density and transition features. Script is then determined by
using Rule based classifiers and its sub classifiers containing set of classification rules which
are raised from the vectors. The proposed system identifies scripts from document images
even if it suffers from noise and other kinds of distortions. Results from experiments,
simulations, and human vision encounter that the proposed technique identifies scripts and numerals with minimal pre-processing and high accuracy. In future, this can also be extended for other scripts.
Optical Character Recognition System for Urdu (Naskh Font)Using Pattern Match...CSCJournals
The offline optical character recognition (OCR) for different languages has been developed over the recent years. Since 1965, the US postal service has been using this system for automating their services. The range of the applications under this area is increasing day by day, due to its utility in almost major areas of government as well as private sector. This technique has been very useful in making paper free environment in many major organizations as far as the backup of their previous file record is concerned. Our this system has been proposed for the Offline Character Recognition for Isolated Characters of Urdu language, as Urdu language forms words by combining Isolated Characters. Urdu is a cursive language, having connected characters making words. The major area of utility for Urdu OCR will be digitizing of a lot of literature related material already stocked in libraries. Urdu language is famous and spoken in more than 3 big countries including Pakistan, India and Bangladesh. A lot of work has been done in Urdu poetry and literature up to the recent century. Creation of OCR for Urdu language will make an important role in converting all those work from physical libraries to electronic libraries. Most of the stuff already placed on internet is in the form of images having text, which took a lot of space to transfer and even read online. So the need of an Urdu OCR is a must. The system is of training system type. It consists of the image preprocessing, line and character segmentation, creation of xml file for training purpose. While Recognition system includes taking xml file, the image to be recognized, segment it and creation of chain codes for character images and matching with already stored in xml file. The system has been implemented and it has 89% recognition accuracy with a 15 char/sec recognition rate.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
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.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
The Presentation is about Optical Character Recognition, Talks about high technology devices such as Bar-code scanner, book readers, Image to Tech Converter and paper Scanners
An Efficient Segmentation Technique for Machine Printed Devanagiri Script: Bo...iosrjce
Segmentation technique plays a major role in scripting the documents for extraction of various
features. Many researchers are doing various research works in this field to make the segmenting process
simple as well as efficient. In this paper a simple segmentation technique for both the line and word
segmentation of a script document has been proposed. The main objective of this technique is to recognize the
spaces that separate two text lines.For the Word segmentation technique also similar procedure is followed. In
this work ,three different scanned document have been taken as input images for both line and word
segmentation techniques. The results found were outstanding with average accuracy for both line and word. It
provides 100% accuracy for line segmentation and 100% for line segmentation as well. Evaluation results show
that our method outperforms several competing methods.
A Simple Segmentation Approach for Unconstrained Cursive Handwritten Words in...CSCJournals
This paper presents a new, simple and fast approach for character segmentation of unconstrained handwritten words. The developed segmentation algorithm over-segments in some cases due to the inherent nature of the cursive words. However the over segmentation is minimum. To increase the efficiency of the algorithm an Artificial Neural Network is trained with significant amount of valid segmentation points for cursive words manually. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison benchmark database IAM is used. The experimental results are encouraging.
This paper presents an approach to recognize off-line Bangla numeral. Today there are many OCR used to recognize
Bangla numeral. The recognition of handwritten character is still a challenging work in the field of pattern recognition. Numeral
recognition in pattern recognition is the process to identify the given character according to the predefined character set. The difficulties
of recognition of handwritten Bangla numeral are that they are different in shapes and sizes which are much curved in nature. . We try
to establish a process to recognize such handwritten Bangla numerals having different shape and size. The input scanned image is first
to be binarized. Then we have segmented all the ten digits of Bangla numerals to identify each and individual digit from a scanned
image. We have used line segmentation to extract the feature from each numeral based on templates. A high correlation coefficient
method provides a successful match between the test data and training data.
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
Performance of Statistics Based Line Segmentation System for Unconstrained H...AM Publications
Handwritten character recognition is a technique by which a computer system could recognize characters and other symbols written in natural handwriting. Segmentation decomposes the document image into subcomponents like lines, words and characters. To achieve greater accuracy, segmentation and recognition could not be treated independently. Most of the existing line segmentation methods have limitations when applied to unconstrained handwritten documents. Statistics based line segmentation system was developed in Java Developer Kit 1.6 for segmenting unconstrained handwritten document images into lines. Arithmetic mean, trimmed mean and inter-quartile mean were used appropriately to achieve accurate segmentation results. The performance of the system was studied by using a few public handwritten document image datasets and images collected from different writers to compare its segmentation accuracy. The datasets contained well separated, sharing, touching, overlapping, irregular base and short handwritten text lines. The samples from the datasets were also segmented by a few other line segmentation methods. The segmentation accuracy of the system was higher than that of other methods. Performance measures like language support, segmentation document and line type of the system were compared with that of other line segmentation methods. The developed system segmented handwritten and printed lines from English, Chinese and Bengali languages and supported linear and non linear lines.
ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORKijaia
This article presents the development of an Arabic online handwriting recognition system. To develop our
system, we have chosen the neural network approach. It offers solutions for most of the difficulties linked
to Arabic script recognition. We test the approach with our collected databases. This system shows a good
result and it has a high accuracy (98.50% for characters, 96.90% for words).
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.
An Optical Character Recognition for Handwritten Devanagari ScriptIJERA Editor
Optical Character Recognition is process of recognition of character from scanned document and lots of OCR now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters . There are no sufficient number of work on Indian language script like Devanagari so this paper present a review on optical character recognition on handwritten Devanagari script
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
Angular Symmetric Axis Constellation Model for Off-line Odia Handwritten Char...IJAAS Team
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Basically handwriting recognition can be divided into two parts as Offline handwriting recognition and Online handwriting recognition. Highly accurate output with predefined constraints can be given by Online handwriting recognition system as it is related to size of vocabulary and writer dependency, printed writing style etc. Hidden markov model increases the success rate of online recognition system. Online handwriting recognition gives additional time information which is not present in Offline system. A Markov process is a random prediction process whose future behavior rely only on its present state, does not depend on the past state. Which means it should satisfy the Markov condition. A Hidden markov model (HMM) is a statistical markov model. In HMM model the system being modeled is assumed to be a markov process with hidden states. Hidden Markov models (HMMs) can be viewed as extensions of discrete-state Markov processes. Human-machine interaction can be drastically getting improved as On-line handwriting recognition technology contains that capability. As instead of using keyboard any person can write anything by hand with the help of digital pen or any similar equipment would be more natural. HMM build a effective mathematical models for characterizing the variance both in time and signal space presented in speech signal.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Character Recognition (Devanagari Script)IJERA Editor
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Character recognition for bi lingual mixed-type characters using artificial n...eSAT Publishing House
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
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.