OCR Projects using Python for CSE/IT Students
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OCR
Artificial neural networks are commonly used in optical character recognition algorithms due to their flexibility, ability to learn, and power. ANNs work by taking an input, running it through a network of neurons arranged in layers, and producing an output. They can be trained to recognize patterns through a learning stage where they are given many examples of input and output pairs. Once trained, ANNs can accurately evaluate new inputs and recognize characters at a 98% rate with only 5% error. Common types of ANNs include feedforward, recurrent, radial basis function, and self-organizing networks.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES
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.
Deep learning: the final frontier for time series analysis and signal process...
- Deep learning, specifically convolutional neural networks and recurrent neural networks, provides alternatives to traditional time series and signal processing techniques that can learn patterns without specifying a model.
- CNNs can learn local geometrical patterns in time series while retaining information over time. RNNs can learn long-range dependencies in high-dimensional data.
- Deep learning approaches like autoencoders are well-suited for tasks like pattern matching, anomaly detection, and simulation/generation of time series. Hybrid approaches that combine hand-crafted features with deep learning may also be effective.
Design and implementation of optical character recognition using template mat...
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
OCR, OMR and Track Ball Working Principals
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
Optical character recognition (ocr) ppt
The document discusses optical character recognition (OCR), which is the process of converting scanned images of printed or handwritten text into machine-encoded text. It provides a brief history of OCR, explaining some of the early developments. It also outlines the typical steps involved, including pre-processing, character recognition, and post-processing. Examples of applications of OCR technology are given.
Artificial neural networks decode brain activity
An artificial neural network was able to decode brain activity signals measured by EEG during both performed and imagined movements. The self-learning algorithm was able to recognize patterns in the brain signals without being provided characteristics beforehand, working as quickly as traditional predetermined systems. The researchers believe this approach could help with early seizure detection, improving communication for paralyzed patients, and aiding neurological diagnosis.
OCR Text Extraction
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.
Recommended
OCR
Artificial neural networks are commonly used in optical character recognition algorithms due to their flexibility, ability to learn, and power. ANNs work by taking an input, running it through a network of neurons arranged in layers, and producing an output. They can be trained to recognize patterns through a learning stage where they are given many examples of input and output pairs. Once trained, ANNs can accurately evaluate new inputs and recognize characters at a 98% rate with only 5% error. Common types of ANNs include feedforward, recurrent, radial basis function, and self-organizing networks.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES
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.
Deep learning: the final frontier for time series analysis and signal process...
- Deep learning, specifically convolutional neural networks and recurrent neural networks, provides alternatives to traditional time series and signal processing techniques that can learn patterns without specifying a model.
- CNNs can learn local geometrical patterns in time series while retaining information over time. RNNs can learn long-range dependencies in high-dimensional data.
- Deep learning approaches like autoencoders are well-suited for tasks like pattern matching, anomaly detection, and simulation/generation of time series. Hybrid approaches that combine hand-crafted features with deep learning may also be effective.
Design and implementation of optical character recognition using template mat...
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
OCR, OMR and Track Ball Working Principals
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
Optical character recognition (ocr) ppt
The document discusses optical character recognition (OCR), which is the process of converting scanned images of printed or handwritten text into machine-encoded text. It provides a brief history of OCR, explaining some of the early developments. It also outlines the typical steps involved, including pre-processing, character recognition, and post-processing. Examples of applications of OCR technology are given.
Artificial neural networks decode brain activity
An artificial neural network was able to decode brain activity signals measured by EEG during both performed and imagined movements. The self-learning algorithm was able to recognize patterns in the brain signals without being provided characteristics beforehand, working as quickly as traditional predetermined systems. The researchers believe this approach could help with early seizure detection, improving communication for paralyzed patients, and aiding neurological diagnosis.
OCR Text Extraction
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.
Product Recognition using Label and Barcodes
This document presents a proposed system to help blind individuals identify products using label text recognition and barcode scanning. The system uses a camera to capture images of product labels and barcodes. It then applies computer vision and image processing techniques like MSER, Canny edge detection, and OCR to the images to extract the label text or recognize the barcode. For labels, the text is converted to speech using a microcontroller and audio output. For barcodes, the number is searched in a database to find matching product details, which are then announced. The goal is to help blind people independently identify common consumer products through automated reading of printed text and barcodes on the items.
Visual Product Identification For Blind Peoples
1) The document presents a prototype system to assist blind people in reading printed text on handheld objects. It uses a camera, data processing, and audio output.
2) A motion-based method is used to isolate the object of interest from other objects in the camera view. Text localization and recognition algorithms are then used to extract and identify text from the isolated region.
3) The system is evaluated on its ability to localize and recognize text from images of objects with complex backgrounds, and on its usability with blind users. Future work will focus on improving text localization and the user interface.
An approach for text detection and reading of product label for blind persons
This document summarizes a research paper that proposes a camera-based assistive reading system to help blind individuals read text on hand-held objects. The system uses a motion-based method to detect the object of interest in the camera view. It then applies text localization and recognition algorithms to extract and identify text from the object. The system architecture includes components for scene capture, data processing, and audio output of recognized text. The proposed system aims to address challenges blind users face in positioning objects for cameras and automatically extracting text information from complex backgrounds. It is projected to be implemented in phases over a scheduled timeline.
IRJET- Design and Development of Tesseract-OCR Based Assistive System to Conv...
The document describes the design and development of an assistive system using Tesseract optical character recognition (OCR) and a Raspberry Pi to convert captured text into voice output for visually impaired users. A Raspberry Pi with a webcam or mobile camera is used to focus on and capture printed text. The text is processed using OCR and segmentation before feature extraction and character recognition with Tesseract. Recognized text characters are converted to audio format using Festival so they can be accessed by blind users. An ultrasonic sensor is also included to allow users to determine the type of object, such as a menu, being interacted with. The system aims to provide faster reading access compared to braille.
Smart Assistant for Blind Humans using Rashberry PI
An OCR (Optical Character Recognition) system which is a branch of computer vision and in turn a sub-class of Artificial Intelligence. Optical character recognition is the translation of optically scanned bitmaps of printed or hand-written text into audio output by using of Raspberry pi. OCRs developed for many world languages are already under efficient use. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. A text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object, a text localization and Tesseract algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binaries and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device on which recognize character using Tesseract algorithm and python programming, the audio output is listed. Abish Raj. M. S | Manoj Kumar. A. S | Murali. V"Smart Assistant for Blind Humans using Rashberry PI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11498.pdf http://www.ijtsrd.com/computer-science/embedded-system/11498/smart-assistant-for-blind-humans-using-rashberry-pi/abish-raj-m-s
Reading System for the Blind PPT
This document summarizes a student project to develop a reading system for blind people using optical character recognition and a braille glove. The system uses a webcam to capture text, an OCR software to recognize the text, and transmits the text to a braille glove using a microcontroller circuit board. The project was developed in two stages - using a laptop and webcam, and then modifying it to use a smartphone's camera and OCR software to make it more portable. The document provides details on the objectives, components, software, and development process of the assistive reading system.
IRJET- Text Recognization of Product for Blind Person using MATLAB
This document presents a camera-based text recognition system developed using MATLAB to assist blind or visually impaired individuals in reading product labels and text from handheld objects. The system uses a camera to capture an image of a product label, applies preprocessing techniques like grayscale conversion and thresholding to extract the text, then uses optical character recognition (OCR) to identify the text and provide it in an audio output. Key steps include text detection using maximally stable extremal regions (MSER) and stroke width transform algorithms, geometric filtering to remove non-text regions, and template matching OCR. The system aims to improve on previous methods by handling more complex backgrounds and text orientations like vertical text. Future work may include recognizing text from more challenging
Product Label Reading System for visually challenged people
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.
Colorful Modern Group Project Creative Presentation.pdf
This document summarizes a machine learning based blind text reading system using optical character recognition (OCR) algorithms. The system aims to develop a solution to enhance accessibility of printed and handwritten text for visually impaired individuals. It uses real-time image acquisition, preprocessing, OCR text extraction and text-to-speech conversion for an effective reading experience. The system explores user interface and algorithm robustness in extracting and reading text, using voice commands, haptic feedback and audio cues.
Optical Character Recognition (OCR) System
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.
D017222226
1. The document discusses an optical character recognition (OCR) system that uses a neural network to recognize handwritten English characters and numerals.
2. It describes the background of OCR, including offline vs online recognition. The key steps of OCR systems are discussed as image acquisition, preprocessing, feature extraction, training and recognition, and post processing.
3. Neural networks are described as being useful for pattern recognition problems like character classification. The proposed system uses a grid infrastructure to allow multi-lingual OCR and more efficient document processing compared to other methods.
IRJET- Review on Text Recognization of Product for Blind Person using MATLAB
This document summarizes a research paper that proposes a system to help blind people read text on product labels and documents using a camera and MATLAB software. The system uses image processing techniques like converting images to grayscale, binarization, and filtering to isolate text from complex backgrounds. It then applies optical character recognition to identify the text and provide information to blind users. The proposed system aims to address limitations of prior methods that struggled with non-horizontal text, complex backgrounds, and positioning objects in the camera view. It extracts a region of interest around a product using motion detection and recognizes text regardless of orientation.
Optical character recognization word
1. The document discusses an optical character recognition (OCR) system based on a grid infrastructure that aims to recognize characters from scanned documents and convert them to electronic format more effectively.
2. The proposed OCR system is divided into five modules: document processing, system training, document recognition, document editing, and document searching.
3. A feasibility study is conducted to analyze the technical, operational, and economic feasibility of the proposed OCR system. Training methods like supervised and unsupervised training are also discussed.
Recognizing of Text and Product Label from Hand Held Entity Intended for Visi...
Our proposed work involves recognizing text and product label reading from portable entities intended for Visionless Persons using Raspberry Pi 3, ultrasonic sensor. Raspberry Pi 3 is the controller used in the proposed device. GPS is fixed in the system and it is used to find the exact location of the person in terms of longitude and latitude, this information is sent to the caretaker through e mail. The caretaker can use the latitude and longitude to find the address on Google Maps. The camera is used to identify the obstacle or object ahead and the output is told to the blind user in speech form. The camera also identifies objects with words on them, using image processing these images are converted to text, and using Tesseract the text is converted to speech, thus giving the speech output to the blind about what is written on the object. RF ID is used to find the stick using tags. The buzzer goes ON to identify the location of the stick. A threshold value for distance between the user and the stick is set, when the distance is less than the threshold value, the buzzer sound increases. Arunkumar. V | Aswin M. D | Bhavan. S | Gopinath. V | Dr. Kishorekumar. A "Recognizing of Text and Product Label from Hand Held Entity Intended for Visionless Persons" 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/ijtsrd39808.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39808/recognizing-of-text-and-product-label-from-hand-held-entity-intended-for-visionless-persons/arunkumar-v
Project report of OCR Recognition
To develop character recognition software system to perform Document Image Analysis which transforms documents in paper format to electronic format
New Technology
This document proposes a portable camera-based system to assist blind persons in reading text labels on products. The system uses a camera to capture images of objects held by the user, detects the region of interest containing text, recognizes the text using OCR, and outputs the text verbally to the user. It aims to enhance independent living for the blind by allowing them to identify product labels and packages. Future work includes improving text localization for various text orientations and sizes and enhancing the user interface.
IRJET- Text Reading for Visually Impaired Person using Raspberry Pi
1) The document presents a system to help visually impaired people read text using optical character recognition and text-to-speech conversion on a Raspberry Pi.
2) The system uses a camera mounted on glasses to take images of text, which are then processed using OCR and converted to audio using e-Speak for the user to hear.
3) It aims to allow visually impaired people to independently read text on product labels, documents and other materials by carrying a portable device that recognizes text in images and converts it to audio in real time.
A Robust Embedded Based String Recognition for Visually Impaired People
A Robust Embedded Based String Recognition for Visually Impaired PeopleAssociation of Scientists, Developers and Faculties
This paper presents the development of a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. Recent developments in computer vision, digital cameras, and portable computers make it feasible to assist these individuals by developing camera-based products that combine computer vision technology with other existing commercial products such optical character recognition (OCR) systems. To automatically extract the text regions from the object, we propose a artificial neural network algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are binarized for processing the algorithm and the text characters are recognized by off-the-shelf OCR (Optical Character Recognition) and other process involved . Now the binarized signals are converted to audible signal. The working principle is as follows first the respected image will be captured and then it is converted to binary signals. Now the image is diagnosed to find whether the text is present in the image. Secondly, if the text is present, then the object of interest is detected. The respected text of the image is recognized and then converted to audible signals. Thus the recognized text codes are given as speech to the user.Hand Written Character Recognition Using Neural Networks
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
final year projects in chennai
The document discusses several emerging embedded technologies in 2018-19 including augmented reality, digital pills, wireless underground sensor networks, virtual reality, Li-Fi, wearable biometric authentication, machine learning, and more. It also lists several domains related to embedded technologies like electrical/renewable energy, biomedical, wireless, robotics, PLC, LiFi, Bluetooth, RSSI, NFC, Raspberry Pi, Internet of Things, virtual reality, GPS/GSM, and Android. Finally, it provides over 50 sample embedded technology project codes related to these domains.
Embedded with IOT project titles 2018 - 2019 in Chennai
The document discusses several emerging embedded technologies in 2018-19 including augmented reality, digital pills, wireless underground sensor networks, virtual reality, Li-Fi, wearable biometric authentication, machine learning, and more. It also lists several domains related to embedded technologies like electrical/renewable energy, biomedical, wireless, robotics, PLC, LiFi, Bluetooth, RSSI, NFC, Raspberry Pi, Internet of Things, virtual reality, GPS/GSM, and Android. Finally, it provides over 50 sample embedded technology project codes related to these domains.
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This document presents a proposed system to help blind individuals identify products using label text recognition and barcode scanning. The system uses a camera to capture images of product labels and barcodes. It then applies computer vision and image processing techniques like MSER, Canny edge detection, and OCR to the images to extract the label text or recognize the barcode. For labels, the text is converted to speech using a microcontroller and audio output. For barcodes, the number is searched in a database to find matching product details, which are then announced. The goal is to help blind people independently identify common consumer products through automated reading of printed text and barcodes on the items.
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This document summarizes a research paper that proposes a camera-based assistive reading system to help blind individuals read text on hand-held objects. The system uses a motion-based method to detect the object of interest in the camera view. It then applies text localization and recognition algorithms to extract and identify text from the object. The system architecture includes components for scene capture, data processing, and audio output of recognized text. The proposed system aims to address challenges blind users face in positioning objects for cameras and automatically extracting text information from complex backgrounds. It is projected to be implemented in phases over a scheduled timeline.
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The document describes the design and development of an assistive system using Tesseract optical character recognition (OCR) and a Raspberry Pi to convert captured text into voice output for visually impaired users. A Raspberry Pi with a webcam or mobile camera is used to focus on and capture printed text. The text is processed using OCR and segmentation before feature extraction and character recognition with Tesseract. Recognized text characters are converted to audio format using Festival so they can be accessed by blind users. An ultrasonic sensor is also included to allow users to determine the type of object, such as a menu, being interacted with. The system aims to provide faster reading access compared to braille.
Smart Assistant for Blind Humans using Rashberry PI
An OCR (Optical Character Recognition) system which is a branch of computer vision and in turn a sub-class of Artificial Intelligence. Optical character recognition is the translation of optically scanned bitmaps of printed or hand-written text into audio output by using of Raspberry pi. OCRs developed for many world languages are already under efficient use. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. A text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object, a text localization and Tesseract algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binaries and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device on which recognize character using Tesseract algorithm and python programming, the audio output is listed. Abish Raj. M. S | Manoj Kumar. A. S | Murali. V"Smart Assistant for Blind Humans using Rashberry PI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11498.pdf http://www.ijtsrd.com/computer-science/embedded-system/11498/smart-assistant-for-blind-humans-using-rashberry-pi/abish-raj-m-s
Reading System for the Blind PPT
This document summarizes a student project to develop a reading system for blind people using optical character recognition and a braille glove. The system uses a webcam to capture text, an OCR software to recognize the text, and transmits the text to a braille glove using a microcontroller circuit board. The project was developed in two stages - using a laptop and webcam, and then modifying it to use a smartphone's camera and OCR software to make it more portable. The document provides details on the objectives, components, software, and development process of the assistive reading system.
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This document presents a camera-based text recognition system developed using MATLAB to assist blind or visually impaired individuals in reading product labels and text from handheld objects. The system uses a camera to capture an image of a product label, applies preprocessing techniques like grayscale conversion and thresholding to extract the text, then uses optical character recognition (OCR) to identify the text and provide it in an audio output. Key steps include text detection using maximally stable extremal regions (MSER) and stroke width transform algorithms, geometric filtering to remove non-text regions, and template matching OCR. The system aims to improve on previous methods by handling more complex backgrounds and text orientations like vertical text. Future work may include recognizing text from more challenging
Product Label Reading System for visually challenged people
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.
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This document summarizes a machine learning based blind text reading system using optical character recognition (OCR) algorithms. The system aims to develop a solution to enhance accessibility of printed and handwritten text for visually impaired individuals. It uses real-time image acquisition, preprocessing, OCR text extraction and text-to-speech conversion for an effective reading experience. The system explores user interface and algorithm robustness in extracting and reading text, using voice commands, haptic feedback and audio cues.
Optical Character Recognition (OCR) System
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.
D017222226
1. The document discusses an optical character recognition (OCR) system that uses a neural network to recognize handwritten English characters and numerals.
2. It describes the background of OCR, including offline vs online recognition. The key steps of OCR systems are discussed as image acquisition, preprocessing, feature extraction, training and recognition, and post processing.
3. Neural networks are described as being useful for pattern recognition problems like character classification. The proposed system uses a grid infrastructure to allow multi-lingual OCR and more efficient document processing compared to other methods.
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This document summarizes a research paper that proposes a system to help blind people read text on product labels and documents using a camera and MATLAB software. The system uses image processing techniques like converting images to grayscale, binarization, and filtering to isolate text from complex backgrounds. It then applies optical character recognition to identify the text and provide information to blind users. The proposed system aims to address limitations of prior methods that struggled with non-horizontal text, complex backgrounds, and positioning objects in the camera view. It extracts a region of interest around a product using motion detection and recognizes text regardless of orientation.
Optical character recognization word
1. The document discusses an optical character recognition (OCR) system based on a grid infrastructure that aims to recognize characters from scanned documents and convert them to electronic format more effectively.
2. The proposed OCR system is divided into five modules: document processing, system training, document recognition, document editing, and document searching.
3. A feasibility study is conducted to analyze the technical, operational, and economic feasibility of the proposed OCR system. Training methods like supervised and unsupervised training are also discussed.
Recognizing of Text and Product Label from Hand Held Entity Intended for Visi...
Our proposed work involves recognizing text and product label reading from portable entities intended for Visionless Persons using Raspberry Pi 3, ultrasonic sensor. Raspberry Pi 3 is the controller used in the proposed device. GPS is fixed in the system and it is used to find the exact location of the person in terms of longitude and latitude, this information is sent to the caretaker through e mail. The caretaker can use the latitude and longitude to find the address on Google Maps. The camera is used to identify the obstacle or object ahead and the output is told to the blind user in speech form. The camera also identifies objects with words on them, using image processing these images are converted to text, and using Tesseract the text is converted to speech, thus giving the speech output to the blind about what is written on the object. RF ID is used to find the stick using tags. The buzzer goes ON to identify the location of the stick. A threshold value for distance between the user and the stick is set, when the distance is less than the threshold value, the buzzer sound increases. Arunkumar. V | Aswin M. D | Bhavan. S | Gopinath. V | Dr. Kishorekumar. A "Recognizing of Text and Product Label from Hand Held Entity Intended for Visionless Persons" 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/ijtsrd39808.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39808/recognizing-of-text-and-product-label-from-hand-held-entity-intended-for-visionless-persons/arunkumar-v
Project report of OCR Recognition
To develop character recognition software system to perform Document Image Analysis which transforms documents in paper format to electronic format
New Technology
This document proposes a portable camera-based system to assist blind persons in reading text labels on products. The system uses a camera to capture images of objects held by the user, detects the region of interest containing text, recognizes the text using OCR, and outputs the text verbally to the user. It aims to enhance independent living for the blind by allowing them to identify product labels and packages. Future work includes improving text localization for various text orientations and sizes and enhancing the user interface.
IRJET- Text Reading for Visually Impaired Person using Raspberry Pi
1) The document presents a system to help visually impaired people read text using optical character recognition and text-to-speech conversion on a Raspberry Pi.
2) The system uses a camera mounted on glasses to take images of text, which are then processed using OCR and converted to audio using e-Speak for the user to hear.
3) It aims to allow visually impaired people to independently read text on product labels, documents and other materials by carrying a portable device that recognizes text in images and converts it to audio in real time.
A Robust Embedded Based String Recognition for Visually Impaired People
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This paper presents the development of a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. Recent developments in computer vision, digital cameras, and portable computers make it feasible to assist these individuals by developing camera-based products that combine computer vision technology with other existing commercial products such optical character recognition (OCR) systems. To automatically extract the text regions from the object, we propose a artificial neural network algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are binarized for processing the algorithm and the text characters are recognized by off-the-shelf OCR (Optical Character Recognition) and other process involved . Now the binarized signals are converted to audible signal. The working principle is as follows first the respected image will be captured and then it is converted to binary signals. Now the image is diagnosed to find whether the text is present in the image. Secondly, if the text is present, then the object of interest is detected. The respected text of the image is recognized and then converted to audible signals. Thus the recognized text codes are given as speech to the user.Hand Written Character Recognition Using Neural Networks
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
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OCR Projects using Python for CSE/IT Students
- 1. AN DEVELOPMENTAL APPROACH OF COLLABORATIVE NEURAL NETWORK &OCR BASED ASSISTIVE SYSTEM FOR TEXT DETECTION WITH VOICE OUTPUT Abstract: Reading is apparently essential in today’s society. Printed text everywhere in the form of reports, receipts, bank statements, classroom handouts, instructions on medicine bottles, etc. And while optical aids, video magnifiers, and screen readers can help blind users and those with low vision to access, there documents. There are few devices that can provide good common hand-held objects printed with text such as invention packages. Objects printed with text such as prescription medication bottles. The most well-known answers for tag restriction in computerized pictures are through the execution of edge extraction, morphological administrators, and Sobel administrator. An edge methodology is ordinarily straightforward and quick. Sobel administrator for edge discovery gives constructive outcomes on the picture. The confinement of tags through morphologically based methodologies is not defenseless to clamor but rather is moderate in execution. After the limitation of the tag comes the character division process. Normal character division procedures depend on histogram investigation and thresholding. Other late methodologies proposed are the utilization of counterfeit neural systems. Existing System: • Today, there are already a few systems that have some promise for portable use, but they cannot handle product labeling. For example, portable bar code readers designed to help blind people identify different products in an extensive product database can enable users who are blind to access information about these products. • But a big limitation is that it is very hard for blind users to find the position of the bar code and to correctly point the bar code reader at the bar code.
- 2. ProposedSystem: Our proposed project automatically focus the text regions from the object, we offer a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels using artificial neural network. Text characters in the localized text regions are then binarized and recognized by off-the-shelf optical character identification software. The renowned text codes are converted into audio output to the blind users. Object distance measure using ultrasonic sensor. Automatically focus the text regions from the object. Text extraction using neural network based OCR. Text to voice conversion using phonematic concatenation for visually impaired people. Block Diagram:
- 3. Hardware required: Raspberry pi Web Camera Relay Ultrasonic sensor HDMI Converter Software Used: • Python IDLE • Raspian jessie OS • Machine learning Library • Opencv • Neural OCR