The document discusses optical character recognition (OCR) and provides details about OCR systems and processes. It summarizes OCR functions, requirements, advantages, and disadvantages. It also outlines OCR operation stages including scanning, recognition, verification, and image manipulation. Guidelines for questionnaire design and preparation for OCR are also provided. Country experiences using OCR for censuses are briefly described.
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
IRJET - Efficient Approach for Number Plaque Accreditation System using W...IRJET Journal
This document presents a proposed efficient approach for number plaque recognition system using Android devices. It discusses using optical character recognition and image processing techniques to extract vehicle number plates from images and recognize the characters. The system is designed to identify vehicles for applications like toll plazas, parking areas, and secure areas by automatically recognizing license plates from moving vehicles. It compares different methods like template matching and neural networks for the character recognition component. The proposed system aims to provide a user-friendly Android application to enable contactless verification of vehicle documents using Aadhaar card numbers, reducing the need to manually carry documents. It is intended to improve security, identify vehicles violating traffic rules, and reduce the complexity and time of existing systems.
IRJET- Survey Paper: Image Reader for Blind PersonIRJET Journal
This document describes a system that uses image processing and optical character recognition to convert text images into audio that can be understood by blind or visually impaired people. The system takes an image as input, extracts the text regions using computer vision techniques, recognizes the characters using OCR, and converts the text to speech. It is implemented using Python libraries like OpenCV, PyTesseract and speech recognition. The system provides functions like image to speech conversion, object counting in images, and cropping text regions for recognition. It is presented as an accessible reading aid for people who are visually impaired, illiterate or have learning disabilities.
IRJET- Scandroid: A Machine Learning Approach for Understanding Handwritten N...IRJET Journal
This document proposes the development of an application called Scandroid that uses machine learning to understand handwritten notes. Scandroid would allow for the scanning of handwritten notes, converting them to text using optical character recognition (OCR) with machine learning models to understand different writing styles and strokes. It would reduce the effort for professionals to copy notes by automating the process. The proposed system is intended to improve knowledge transfer for professionals in India by making notes portable and easily transferable in a digital format. It aims to incorporate features like QR code scanning, converting images to PDF, and recognizing both typed and handwritten text through OCR.
Correcting optical character recognition result via a novel approachIJICTJOURNAL
Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques to improve recognition of degraded text.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques for English, Indian languages, license plate recognition, document binarization, and removing "bleed-through" effects from financial documents.
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
IRJET - Efficient Approach for Number Plaque Accreditation System using W...IRJET Journal
This document presents a proposed efficient approach for number plaque recognition system using Android devices. It discusses using optical character recognition and image processing techniques to extract vehicle number plates from images and recognize the characters. The system is designed to identify vehicles for applications like toll plazas, parking areas, and secure areas by automatically recognizing license plates from moving vehicles. It compares different methods like template matching and neural networks for the character recognition component. The proposed system aims to provide a user-friendly Android application to enable contactless verification of vehicle documents using Aadhaar card numbers, reducing the need to manually carry documents. It is intended to improve security, identify vehicles violating traffic rules, and reduce the complexity and time of existing systems.
IRJET- Survey Paper: Image Reader for Blind PersonIRJET Journal
This document describes a system that uses image processing and optical character recognition to convert text images into audio that can be understood by blind or visually impaired people. The system takes an image as input, extracts the text regions using computer vision techniques, recognizes the characters using OCR, and converts the text to speech. It is implemented using Python libraries like OpenCV, PyTesseract and speech recognition. The system provides functions like image to speech conversion, object counting in images, and cropping text regions for recognition. It is presented as an accessible reading aid for people who are visually impaired, illiterate or have learning disabilities.
IRJET- Scandroid: A Machine Learning Approach for Understanding Handwritten N...IRJET Journal
This document proposes the development of an application called Scandroid that uses machine learning to understand handwritten notes. Scandroid would allow for the scanning of handwritten notes, converting them to text using optical character recognition (OCR) with machine learning models to understand different writing styles and strokes. It would reduce the effort for professionals to copy notes by automating the process. The proposed system is intended to improve knowledge transfer for professionals in India by making notes portable and easily transferable in a digital format. It aims to incorporate features like QR code scanning, converting images to PDF, and recognizing both typed and handwritten text through OCR.
Correcting optical character recognition result via a novel approachIJICTJOURNAL
Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques to improve recognition of degraded text.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques for English, Indian languages, license plate recognition, document binarization, and removing "bleed-through" effects from financial documents.
IRJET- Text Recognization of Product for Blind Person using MATLABIRJET Journal
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 peopleIRJET Journal
1) The document proposes a camera-based assistive text reading system to help blind people read text labels on handheld objects. It uses computer vision techniques like stroke width transform to isolate the object of interest and detect the region of interest.
2) In the region of interest, the system performs text localization using gradient features and edge distributions. It then recognizes text using optical character recognition and outputs it verbally for the user.
3) The system aims to achieve robust text extraction and recognition from complex backgrounds while focusing on usability. It analyzes existing assistive technologies and proposes an improved workflow including image capture, processing, and audio output.
The document describes a system for offline transcription of handwritten text using artificial intelligence. The system takes scanned images of handwritten forms as input. It uses image processing techniques like thresholding and morphological operations to preprocess the images and localize the boxes containing handwritten text. A recurrent neural network model with Tesseract OCR is used for handwritten character recognition. The recognized text is post-processed and stored in an Excel sheet. The system was able to recognize over 80% of characters correctly on test data. Future work may include expanding it to recognize additional languages and improving accuracy for low-quality images.
A Deep Learning Approach to Recognize Cursive HandwritingIRJET Journal
This document presents a deep learning approach for recognizing cursive handwriting. It discusses how cursive handwriting recognition is challenging due to variations in individual writing styles. The proposed system uses a convolutional neural network (CNN) for feature extraction and classification of handwritten characters. It takes input images of handwritten text, performs preprocessing like resizing and segmentation, extracts features using CNN, and classifies characters for recognition. The system is trained on datasets containing cursive text written by different people. It aims to accurately recognize cursive text and convert it to digital text formats like documents. Experimental results show the system achieves high recognition accuracy compared to conventional approaches.
The document summarizes an optical character recognition system for recognizing multi-font English texts. It presents an OCR system that uses discrete cosine transform and wavelet transform for feature extraction. Experiments on 3185 training samples and 13650 testing samples showed wavelet features produced better recognition rates of 96% compared to 92% for DCT features. Further classification of characters by height-to-width ratio improved rates to 99% for wavelet and 95% for DCT. The system aims to automate text recognition from documents to reduce errors and time compared to manual re-typing.
Real Time Character Recognition on FPGA for Braille DevicesIRJET Journal
This document presents a real-time character recognition system implemented on an FPGA for use in braille devices. It trains an artificial neural network in MATLAB to recognize characters from video input. The neural network weights are then hardcoded into the FPGA for classification. An Altera DE2 development board with a Cyclone II FPGA is used. The system successfully recognizes characters from different fonts and could enable advanced portable braille devices to provide access to text in real-time. Future work includes recognizing characters from multiple languages by changing the neural network weights and implementing the full network on the FPGA.
IRJET- Optical Character Recognition using Image ProcessingIRJET Journal
This document discusses optical character recognition (OCR) using image processing. It begins with an abstract that defines OCR as the conversion of typed, handwritten, or printed text into machine-encoded text from scanned documents or photos. The document then outlines the components and steps of a typical OCR system, including optical scanning, preprocessing, segmentation, character extraction, and recognition. It describes using techniques like thresholding, smoothing, projection profiles, clustering algorithms, and creating a character database to classify and recognize characters for conversion to machine-encoded text.
Visual Product Identification For Blind PeoplesIRJET Journal
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.
OPTICAL CHARACTER RECOGNITION IN HEALTHCAREIRJET Journal
This document discusses an optical character recognition (OCR) model for extracting text information from medical records using machine learning and deep learning. The proposed OCR model would speed up access to medical records and ensure data is available electronically with no errors. It would recognize characters in medical form images and convert paper records to electronic format. The document then reviews several related works on OCR, including methods using Tesseract, character recognition models using neural networks, and OCR systems for assisting the visually impaired. It concludes with a discussion of different feature extraction and machine learning methods used for text categorization and character recognition.
IRJET- Intelligent Character Recognition of Handwritten CharactersIRJET Journal
This document summarizes research on intelligent character recognition of handwritten characters using neural networks. It discusses how neural networks can be trained on feature vectors extracted from images to accurately recognize (up to 95%) handwritten alphanumeric characters. The proposed system segments images into characters, extracts features like intersections and endpoints, trains a neural network on feature vectors, and then uses the trained network to recognize new characters. It achieved high accuracy after training on a large dataset of 400 samples. The system automatically transfers recognized text to an Excel sheet.
Optical Recognition of Handwritten TextIRJET Journal
This document discusses the development of an optical character recognition (OCR) software for recognizing handwritten text. It begins with an introduction to OCR and the challenges of handwritten text recognition compared to printed text. The document then outlines the objectives, scope, assumptions, system requirements, design, implementation, and evaluation of the developed OCR software. It describes preprocessing steps, text detection algorithms like Hough transform, and recognition models including convolutional neural networks, bidirectional recurrent neural networks, and connectionist temporal classification. The software achieves OCR through a graphical user interface and utilizes pre-trained models on GPUs to recognize text in uploaded images. Evaluation on sample inputs demonstrates the ability to detect and recognize handwritten words.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
INFORMATION EXTRACTION FROM PRODUCT LABELS: A MACHINE VISION APPROACHijaia
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
This document summarizes a presentation on digitizing and automating document-intensive workflows. It discusses 4 tips: 1) when to digitize based on benchmarking processes and identifying opportunities, 2) leveraging advances in upstream capture through multiple input channels and formats, 3) leveraging advances in downstream capture through automated recognition and routing, and 4) how to plan and manage a capture operation through benchmarking best practices. The presentation provides examples and criteria for benchmarking various aspects of capture operations.
This document discusses optical character recognition (OCR) technology. OCR software scans printed text documents and converts them into editable, machine-readable text files. The document outlines the benefits of OCR such as eliminating manual data entry and improving data accuracy. It also lists key areas where OCR is used including cloud storage, mailroom automation, and banking. Metrics for evaluating OCR software and current trends in OCR technology are discussed. Top OCR providers like Adobe Acrobat, OmniPage, and ABBYY FineReader are compared. Case studies on how OCR is used by accountants and universities are provided.
The document summarizes how a digital pen and paper system works to digitize handwritten notes and forms:
1) A digital pen with a camera records pen strokes on paper printed with invisible dots that encode page locations. As the user writes, the pen registers coordinates of each stroke.
2) The paper forms can be printed on ordinary printers. When completed with the digital pen, handwriting is converted to digital text using recognition software.
3) Data is sent from the pen via Bluetooth to a server for processing into editable digital forms via an optical character recognition program.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
This document provides best practices for digitizing collections. It discusses key questions to consider for a digitization project, the pros and cons of in-house vs outsourced digitization, documentation standards, staffing needs, costs, scanner types, file formats, naming conventions, and storage recommendations. The overall guidelines are to digitize at high resolution from original sources, create master files and derivatives for access, use open standards, and fully document the project for long-term preservation and usability of the digital files.
Automated Identification of Road Identifications using CNN and KerasIRJET Journal
The document proposes a model to automatically detect traffic signs using convolutional neural networks (CNN) and the Keras library, even if the signs are unclear or damaged. It aims to help autonomous vehicles properly identify different types of traffic signs. The methodology involves collecting a dataset of traffic sign images, training a CNN model using Keras, testing the model on new images, and using the trained model to recognize signs from user-provided inputs in real-time. Evaluation metrics like accuracy and loss are plotted to analyze the model's performance. The system is meant to achieve over 95% accuracy in identifying various traffic sign types to assist self-driving cars in safely following traffic rules.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
IRJET- Text Recognization of Product for Blind Person using MATLABIRJET Journal
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 peopleIRJET Journal
1) The document proposes a camera-based assistive text reading system to help blind people read text labels on handheld objects. It uses computer vision techniques like stroke width transform to isolate the object of interest and detect the region of interest.
2) In the region of interest, the system performs text localization using gradient features and edge distributions. It then recognizes text using optical character recognition and outputs it verbally for the user.
3) The system aims to achieve robust text extraction and recognition from complex backgrounds while focusing on usability. It analyzes existing assistive technologies and proposes an improved workflow including image capture, processing, and audio output.
The document describes a system for offline transcription of handwritten text using artificial intelligence. The system takes scanned images of handwritten forms as input. It uses image processing techniques like thresholding and morphological operations to preprocess the images and localize the boxes containing handwritten text. A recurrent neural network model with Tesseract OCR is used for handwritten character recognition. The recognized text is post-processed and stored in an Excel sheet. The system was able to recognize over 80% of characters correctly on test data. Future work may include expanding it to recognize additional languages and improving accuracy for low-quality images.
A Deep Learning Approach to Recognize Cursive HandwritingIRJET Journal
This document presents a deep learning approach for recognizing cursive handwriting. It discusses how cursive handwriting recognition is challenging due to variations in individual writing styles. The proposed system uses a convolutional neural network (CNN) for feature extraction and classification of handwritten characters. It takes input images of handwritten text, performs preprocessing like resizing and segmentation, extracts features using CNN, and classifies characters for recognition. The system is trained on datasets containing cursive text written by different people. It aims to accurately recognize cursive text and convert it to digital text formats like documents. Experimental results show the system achieves high recognition accuracy compared to conventional approaches.
The document summarizes an optical character recognition system for recognizing multi-font English texts. It presents an OCR system that uses discrete cosine transform and wavelet transform for feature extraction. Experiments on 3185 training samples and 13650 testing samples showed wavelet features produced better recognition rates of 96% compared to 92% for DCT features. Further classification of characters by height-to-width ratio improved rates to 99% for wavelet and 95% for DCT. The system aims to automate text recognition from documents to reduce errors and time compared to manual re-typing.
Real Time Character Recognition on FPGA for Braille DevicesIRJET Journal
This document presents a real-time character recognition system implemented on an FPGA for use in braille devices. It trains an artificial neural network in MATLAB to recognize characters from video input. The neural network weights are then hardcoded into the FPGA for classification. An Altera DE2 development board with a Cyclone II FPGA is used. The system successfully recognizes characters from different fonts and could enable advanced portable braille devices to provide access to text in real-time. Future work includes recognizing characters from multiple languages by changing the neural network weights and implementing the full network on the FPGA.
IRJET- Optical Character Recognition using Image ProcessingIRJET Journal
This document discusses optical character recognition (OCR) using image processing. It begins with an abstract that defines OCR as the conversion of typed, handwritten, or printed text into machine-encoded text from scanned documents or photos. The document then outlines the components and steps of a typical OCR system, including optical scanning, preprocessing, segmentation, character extraction, and recognition. It describes using techniques like thresholding, smoothing, projection profiles, clustering algorithms, and creating a character database to classify and recognize characters for conversion to machine-encoded text.
Visual Product Identification For Blind PeoplesIRJET Journal
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.
OPTICAL CHARACTER RECOGNITION IN HEALTHCAREIRJET Journal
This document discusses an optical character recognition (OCR) model for extracting text information from medical records using machine learning and deep learning. The proposed OCR model would speed up access to medical records and ensure data is available electronically with no errors. It would recognize characters in medical form images and convert paper records to electronic format. The document then reviews several related works on OCR, including methods using Tesseract, character recognition models using neural networks, and OCR systems for assisting the visually impaired. It concludes with a discussion of different feature extraction and machine learning methods used for text categorization and character recognition.
IRJET- Intelligent Character Recognition of Handwritten CharactersIRJET Journal
This document summarizes research on intelligent character recognition of handwritten characters using neural networks. It discusses how neural networks can be trained on feature vectors extracted from images to accurately recognize (up to 95%) handwritten alphanumeric characters. The proposed system segments images into characters, extracts features like intersections and endpoints, trains a neural network on feature vectors, and then uses the trained network to recognize new characters. It achieved high accuracy after training on a large dataset of 400 samples. The system automatically transfers recognized text to an Excel sheet.
Optical Recognition of Handwritten TextIRJET Journal
This document discusses the development of an optical character recognition (OCR) software for recognizing handwritten text. It begins with an introduction to OCR and the challenges of handwritten text recognition compared to printed text. The document then outlines the objectives, scope, assumptions, system requirements, design, implementation, and evaluation of the developed OCR software. It describes preprocessing steps, text detection algorithms like Hough transform, and recognition models including convolutional neural networks, bidirectional recurrent neural networks, and connectionist temporal classification. The software achieves OCR through a graphical user interface and utilizes pre-trained models on GPUs to recognize text in uploaded images. Evaluation on sample inputs demonstrates the ability to detect and recognize handwritten words.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
INFORMATION EXTRACTION FROM PRODUCT LABELS: A MACHINE VISION APPROACHijaia
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
This document summarizes a presentation on digitizing and automating document-intensive workflows. It discusses 4 tips: 1) when to digitize based on benchmarking processes and identifying opportunities, 2) leveraging advances in upstream capture through multiple input channels and formats, 3) leveraging advances in downstream capture through automated recognition and routing, and 4) how to plan and manage a capture operation through benchmarking best practices. The presentation provides examples and criteria for benchmarking various aspects of capture operations.
This document discusses optical character recognition (OCR) technology. OCR software scans printed text documents and converts them into editable, machine-readable text files. The document outlines the benefits of OCR such as eliminating manual data entry and improving data accuracy. It also lists key areas where OCR is used including cloud storage, mailroom automation, and banking. Metrics for evaluating OCR software and current trends in OCR technology are discussed. Top OCR providers like Adobe Acrobat, OmniPage, and ABBYY FineReader are compared. Case studies on how OCR is used by accountants and universities are provided.
The document summarizes how a digital pen and paper system works to digitize handwritten notes and forms:
1) A digital pen with a camera records pen strokes on paper printed with invisible dots that encode page locations. As the user writes, the pen registers coordinates of each stroke.
2) The paper forms can be printed on ordinary printers. When completed with the digital pen, handwriting is converted to digital text using recognition software.
3) Data is sent from the pen via Bluetooth to a server for processing into editable digital forms via an optical character recognition program.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
This document provides best practices for digitizing collections. It discusses key questions to consider for a digitization project, the pros and cons of in-house vs outsourced digitization, documentation standards, staffing needs, costs, scanner types, file formats, naming conventions, and storage recommendations. The overall guidelines are to digitize at high resolution from original sources, create master files and derivatives for access, use open standards, and fully document the project for long-term preservation and usability of the digital files.
Automated Identification of Road Identifications using CNN and KerasIRJET Journal
The document proposes a model to automatically detect traffic signs using convolutional neural networks (CNN) and the Keras library, even if the signs are unclear or damaged. It aims to help autonomous vehicles properly identify different types of traffic signs. The methodology involves collecting a dataset of traffic sign images, training a CNN model using Keras, testing the model on new images, and using the trained model to recognize signs from user-provided inputs in real-time. Evaluation metrics like accuracy and loss are plotted to analyze the model's performance. The system is meant to achieve over 95% accuracy in identifying various traffic sign types to assist self-driving cars in safely following traffic rules.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
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05a(1).ppt
1. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Introduction to Optical Character
Recognition (OCR)
2. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Summary
Overview of OCR
System Requirements
Advantages and Disadvantages
Operation and Management
Questionnaire Design and Preparation
OCR Field Operation
OCR Country Outlook
3. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR (Optical Character Recognition)
Function & Features of OCR/ICR
ICR, OCR and OMR Compared
Optical Mark Reader (OMR)
OCR/ ICR
4. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR (Optical Character Recognition)
Also referred to as Optical Character Reader
“…a system that provides a full alphanumeric
recognition of printed or handwritten characters at
electronic speed by simply scanning the form.”(UNESCAP, Pop-IT
project, 1997-2001)
Intelligent Character Recognition (ICR) is used to
describe the process of interpreting image data, in
particular alphanumeric text.
Sometimes OCR is known as ICR
5. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Functions & Features of OCR
Forms can be scanned through a scanner and then the
recognition engine of the OCR system interpret the images
and turn images of handwritten or printed characters into
ASCII data (machine-readable characters).
The technology provides a complete form processing and
documents capture solution.
Allows an open, scaleable and workflow.
Includes forms definition, scanning, image
pre-processing, and recognition capabilities.
6. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
ICR,OCR and OMR Differences
ICR and OCR are recognition engines used with
imaging;
OMR is a data collection technology that does
not require a recognition engine.
OMR cannot recognize hand-printed or
machine-printed characters.
7. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Optical Mark Reader (OMR)
Forms
An OMR works with a specialized document and contains
timing tracks along one edge of the form to indicate scanner
where to read for marks which look like black boxes on the
top or bottom of a form.
The cut of the form is very precise and the bubbles on a form
must be located in the same location on every form.
Storage
With OMR, the image of a document is not scanned and
stored.
Accuracy
OMR is simpler than OCR.
designed properly, OMR has more accuracy than OCR.
8. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR/ ICR
Forms
OCR/ ICR is more flexible since no timing tracks or block
like form IDs required.
The image can float on a page.
ICR/ OCR technology uses registration mark on the four-
corners of a document, in the recognition of an image.
Respondents place one character per box on this form.
The use of drop color reduces the size of the scanner’s
output and enhances the accuracy.
Storage/ retrieval
If the document needs to be electronically stored and
maintained, then OCR/ ICR is needed.
OCR/ICR technologies, images can be scanned, indexed,
and written to optical media.
9. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OMR-OCR/ICR Compared
10. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
System Requirements
Minimum capacity PC Requirements:
Processor: Pentium 200 MHz RAM: 32 MB Disk: 4 GB
Form modules are designed to operate in a batch
processing;
Run under LAN and PC based platforms and take full
advantage of the graphical user interface and 32 bit
processing power available with most Windows
versions.
Software:
OCR with ICR capability software
Questionnaire Design Software
11. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
System Requirements (cont.)
Scanner
OCR scanners with minimum capacity:
Duplex scanning
Speed: 60 sheets/ min
Automatic Document Feeder (ADF):
Scanning can take a significant amount,
and the system lets user scan up without
doing the OCR.
12. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Advantages and Disadvantages
Advantages of Using Images Rather Than Paper
Quicker processing; no moving or storage of questionnaires near
operators
Savings in costs and efficiencies by not having the paper
questionnaires
Scanning and recognition allowed efficient management and planning
for the rest of the processing workload
Reduced long term storage requirements, questionnaires could be
destroyed after the initial scanning, recognition and repair
Quick retrieval for editing and reprocessing
Minimizes errors associated with physical handling of the
questionnaires
13. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Advantages and Disadvantages
Disadvantages of Using Images Rather Than Paper
Accuracy
While OCR technology can be effective in
converting handwritten or typed characters, it
does not give as high accuracy as of OMR for
reading data, where users are actually marking
forms
Additional workload to data collectors OCR has
severe limitations when it comes to human
handwriting
Characters must be hand-printed with separate
characters in boxes
14. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management
OCR Process Stages
Document Scanning process
Scanning speed will be determined by the quality of the
scanner machines, the size of non-drop out color. Paper
quality, cleanness, weights.
Recognizing process
The recognizing process is to interpret images. The right
memory (dictionary) and the configuration threshold will
determine the accuracy of interpretation of the ICR.
Verifying Process
To compare the value of the interpreted image with the real
image of the form.
Processing can be in geographic order or in random order.
15. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management (cont.)
Image Manipulation
Electronic questionnaires can be sent to specialist operators then
back to the original operator if necessary
Same questionnaire can be worked on simultaneously by two or
more persons
Electronic questionnaires are readily available for post census
analysis (easier access to questionnaires)
Parts of various questionnaires on screen at once for inter record
editing
Able to view the relevant field book entry on screen in conjunction
with questionnaires which is helpful for coding and editing
16. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management (cont.)
Coding Assistance
The problems are simpler for the operator to identify
Can use images of questions that will not be captured (scanned but
not recognized) to help the coding process. ex, light pencil.
Operator can magnify images to read characters not discernible to
the naked eye
Appropriate software ensures that the data is validated as the
forms are read.
Checks to ensure selections on a form are filled in.
Possible to distinguish between intended marks and marks that
have been erased.
17. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management (cont.)
OMR Scanner Speed
Factors
Skew: Each document is moved from an automatic
feeder into ascanner and angle of skew is
sometimes introduced.
De-skew: Analyze the image bit- map, calculates
and returns the angle of skew up to +/-25.
Example. De-skew often refer to %, which is the
pixel shift. 10% is a 20-pixel shift in a line of 200
pixels or one tenth of an inch in an inch long line.
18. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management (cont.)
Landscape Detection and Auto Rotation:
landscape detection will automatically detect
and rotate appropriate images 90 degrees.
White Page Detection:
Normally, a double-sided scanner creates two
images per scanners page.
However, if the back or front page is blank,
there is no need to store this image.
White page detection
Allows the user to avoid storing blank page.
19. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Operation and Management (cont.)
Other Factors
Automatic Image Registration
De-Speckle and Shade Removal
Character Enhancer
Cost Savings
Automatic processes to improve recognition
rates
Voting techniques, Multiple engines, Learning
20. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Questionnaire Design and
Preparation
Drop Out Color
Usually red- the color facility in OCR system that
allows the system to pick up only the meaningful
information from an OCR form.
The system doesn't need to know the values
including tick boxes written in the drop out color.
The OCR system only needs to see the black parts,
and compares them to specifications to see parts
that are filled or written.
Characters or Marks
Considering the speed of the data capture process
and to reduce rates, it is advisable to use marks or
“ticks” as much as possible
21. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
Questionnaire Design and
Preparation (cont.)
How to Obtain Good Results of Scanning
Select adequate paper quality; Reliable printing press.
Appropriate ink, considering drop out color, for the questionnaires paper
heavier than 80 grams per square meter can help avoid paper crashes or
over read the other side of a single page.
Form Design Advise
Number items to be included in a form; Design size of boxes for each
character answer carefully.
Define drop out color properly; use registration marks.
Pre-print the codes near the place where the box for ticks are located
Maintain consistent pattern in which the information to be collected will be
located.
Do not disturb the visibility of the ticks and marks with titles, labels or
instructions.
Avoid putting "answers" of one field to another page of the questions;
Avoid using open ended questions
22. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Field Operation
Training for Collection and Processing Staff
Basic software, scanner operations, including
installation and troubleshooting.
Applications with emphasis on the development of
custom applications including: configuring
nonstandard forms
Pre-marking of forms, use of overprinting customize
forms
Processing of surveys
Crating custom outputs file formats
23. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Field Operation (cont.)
Reasons of Error- Reading of OCR
Bad condition of the form because of dirt, folded, crumple, etc.
Forms fed into OCR scanner are not straight (at an angle); Incompletely filled
Reduce Error-Reading of OCR
Checking the questionnaires for completeness and consistencies; Preparation of own memory (dictionary);
Defining permissible margins of OCR reading errors
Particular Care in Writing Numbers or Alphabetic
One box contains only one character; Characters should not extend outside designated boxes; Unnecessary
lines of characters such as points, decorative strokes, hooks, etc. are prohibited. Strokes should not be ended
with flourishes or extensions.
All lines should be connected without breaks; All lines or dots should be pressed with the same pressure.
Value Checking Steps: Verify that the information captured by OMR is the same with the questionnaire
Control for Blank: If the information is blank, what type of control must be taken.
Control steps should be taken if the information image is partial or no information to assure the quality of
generated files.
Missing Questionnaire; Make sure that the entire questionnaires are scanned
completely, no missing and no duplication as well.
Therefore control procedures including to produce control tables to compare with manual work.
24. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Country Outlook
Countries using optical mark recognition
(Greece)
Countries using optical character recognition
(Croatia- in use for the next census round)
(Japan-out-sources entire process and in use for the next
census round)
Countries using both
Belgium
Countries planning to use OCR
Tajikistan
(Tonga) looking to introduce and use OCR for our next Census
25. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Country Outlook
Common device/scanner and software used by
NSOs
(Croatia) KODAK DS3520 bitonal scanners, IBM IFP
(intelligent Forms Processing)
(Greece) OMR- devices/scanners were ‘’axm
990/995’’ with FORM/ AXF/ ADELE+ software
(New Zealand) Kodak scanners i830 and i7620 -
scanning and raw data capture process (recognition
aspect) were outsourced.- For the next census -end
scanning and data capture process will more than
likely be outsourced but it really is a variation to a
current supplier agreement.
(Belgium) AGFA (high resolution) scanner
26. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR in Use
Editing method used for the census
(Japan) cold-deck method, hot-deck method, etc.
(Croatia) in house developed – logical checking and automatic
and manual correcting
(Greece) via PC- editor (officer of N.S.S.G.) confirms or rejects
a non-accurate value or inputs a missing one.
(New Zealand) mixture of micro and macro editing practices.
Individual responses may have range or validity edits, inter-
field edits and also inter-form edits (within a household).
Macro editing is particularly used during the data evaluation
process and data may be reprocessed as a result of this
27. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Country Outlook
Common commercial or free software used in
OCR
(Croatia) Use ACTR (automated coding by text
recognition) for coding -software developed by
Statistics Canada.
(Greece) Commercial software, after an open
bidding, according to the budgetary plan of the
population census
(New Zealand) IBM Intelligent Forms Processing
(IFP) system through an established user agreement.
(Belgium) IRIS (Image Recognition Integrated
Systems)
28. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
OCR Country Outlook
Concerns/issues with the use of optical
character recognition for data capture
for the census?
(Japan) Speed of data capture and recognition,
recognition accuracy of Japanese characters, etc.
(Greece) OMR -related to the optical recognition of
numbers, the rapidity of optical recognition itself
and the electronic storage of the questionnaires.
(Tajikistan) Getting equipment and training.
(Samoa) Not enough financial support and
technical human resources.
29. Workshop on international standards, contemporary technologies and regional cooperation
Noumea, New Caledonia, 4 – 8 February 2008
THANK YOU!