Character Recognition Algorithm Research Articles

Candidate Authentication using OCR Techniques

This paper explores the application of Optical Character Recognition (OCR) techniques for authenticating candidates based on identity documents. As advancements in technology continue to redefine various industries, the integration of OCR into candidate authentication processes offers a streamlined and efficient solution. The aim is to design a system which gets the image of the identity proof and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). The authentication process encompasses document validation and implementation of security measures to safeguard against fraud.By combining YOLO object detection, Optical Character Recognition (OCR) and fuzzy matching technique ,this approach aims to enhance the accuracy and reliability of candidate authentication. This underscores the importance of a comprehensive authentication framework, incorporating both technological and procedural elements, to establish a robust and secure candidate authentication process in diverse contexts. Keywords: ID Verification,Optical Character Recognition (OCR), Text Extraction, YOLO,Object Detection,Image Segmentation, EasyOCR , Fuzzy Matching.

OCTess: AN OPTICAL CHARACTER RECOGNITION ALGORITHM FOR AUTOMATED DATA EXTRACTION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY REPORTS.

Manual extraction of spectral domain optical coherence tomography (SD-OCT) reports is time and resource intensive. This study aimed to develop an optical character recognition (OCR) algorithm for automated data extraction from Cirrus SD-OCT macular cube reports. SD-OCT monocular macular cube reports (n = 675) were randomly selected from a single-center database of patients from 2020 to 2023. Image processing and bounding box operations were performed, and Tesseract (an OCR library) was used to develop the algorithm, OCTess. The algorithm was validated using a separate test data set. The long short-term memory deep learning version of Tesseract achieved the best performance. After reverifying all discrepancies between human and algorithmic data extractions, OCTess achieved accuracies of 100.00% and 99.98% in the training (n = 125) and testing (n = 550) datasets, while the human error rate was 1.11% (98.89% accuracy) and 0.49% (99.51% accuracy) in each, respectively. OCTess extracted data in 3.1 seconds, compared with 94.3 seconds per report for human evaluators. We developed an OCR and machine learning algorithm that extracted SD-OCT data with near-perfect accuracy, outperforming humans in both accuracy and efficiency. This algorithm can be used for efficient construction of large-scale SD-OCT data sets for researchers and clinicians.

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten manuscripts. To tackle the problem, a segmentation-free recognition algorithm based on deep learning network is proposed in this paper. The network consists of four neural layers, including input layer for image preprocessing, CNNs layer for feature extraction, BDLSTM layer for sequence prediction, and connectionist temporal classification layer for text sequence alignment and classification. Besides, a novel data processing method is performed for data length equalization. Based on this, groups of experiments, based on six typical databases, involved in evaluation indicators of character correct rate, training time cost, storage space cost and testing time cost are carried out. The experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.

Deep Learning Algorithms for Arabic Optical Character Recognition: A Survey

In recent years, deep learning has begun to supplant traditional machine learning algorithms in a variety of fields, including machine translation (MT), pattern recognition (PR), natural language processing (NLP), speech recognition (SR), and computer vision. Systems for optical character recognition (OCR) have recently been developed using deep learning techniques with great success. Within the area of pattern recognition and computer vision, the procedure of handwritten character recognition is still considered to be one of the most challenging. The height, orientation, and width of the handwritten characters do not always correspond with one another because different people use different writing instruments and have their own unique writing styles. This makes the job of handwritten recognition challenging and difficult. The regional languages of Arabic and Urdu have received less research. In this article, a summary and comparison of the most significant techniques of deep learning that are used in the recognition of Arabic-adapted scripts like Arabic and Urdu have been provided.

Research on Offline Handwritten Chinese Character Recognition Algorithm Based on Gated Convolution and Stacked Self-Attention

Research on Offline Handwritten Chinese Character Recognition Algorithm Based on Gated Convolution and Stacked Self-Attention

An Improved Algorithm for Optical Character Recognition using Graphical User Interface Design

An Improved Algorithm for Optical Character Recognition using Graphical User Interface Design

Research on Physical Education Management System in Higher Education Institutions in the Context of Deep Learning

Abstract This paper focuses on the management of physical movement sports disciplines and combines relevant algorithms to design an artificial intelligence-based physical education management system framework. In terms of related algorithms, the first is for the research of character recognition algorithms. Considering the intra-class differences between individual movements, the LR-based feature extraction algorithm is proposed. Secondly, for the research of the action evaluation algorithm, the statistical value, DTW parameter action time series difference and correlation coefficient based on the statistical value and DTW parameter are used as the features, combined with SVM-based feature extraction algorithm for perceiving the action differences. Finally, with the help of deep neural networks for movement analysis, we can improve students’ movement techniques during training. In the course schedule supported by the system, the proportion of moderate-intensity exercise time is high, basically, more than 40%, which meets the WHO requirements for high-intensity exercise time in physical education classes. The post-course RPE feedback from students was spread out between 12 and 15, and the highest post-course RPE value was 14.82<unk>1.38, which was evaluated within a reasonable range of intervals. The teaching management system in this paper can meet the requirements of physical training and teaching, and provide quantifiable basis and management tools for the informatization and precise teaching of physical movement subjects.

Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents

Linked data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This article evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods’ parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theater plays’ covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.

Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages

Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

Data Recognition for Multi-Source Heterogeneous Experimental Detection in Cloud Edge Collaboratives

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with traditional manual data detection and recognition. Firstly, a recognition architecture for experimental detection data of intelligent measurement systems is designed based on a cloud-edge collaborative environment to improve the efficiency of data processing. Then, the improved DBNet network is used in the edge computing center to detect the text, and the correction module is used to correct the deviation of the detected text to ensure the standardization of the text. Finally, in the central cloud, the end-to-end indefinite length character recognition (CRNN) algorithm is used to analyze and identify the text order, rules, and other information of the image after the correction is completed, extract the test detection data, and convert the detection data into row data according to the two-dimensional table structure, and conduct structured storage and management through the relational database. An experimental analysis of the proposed method is conducted based on the deep learning framework, and results show that its accuracy rate and recall rate are close to 96% and 94%, respectively, with an average accuracy of 95.09%. This fully demonstrates the proposed method is effective and, therefore, applicable to power equipment experimental detection data recognition.

Digital Image Text Recognition Using Machine Learning Algorithms

Abstract: Text recognition is used in various fields like document analysis, picture labeling, and text analysis, etc. The process of recognizing text from image is very crucial.. Employing Maximally Stable Extremal Regions (MSER) and Optical Character Recognition (OCR) algorithms have drastically changed the accuracy and reliability of text detection from photos. Our proposed work suggests a model for text recognition that makes use of both the MSER and OCR algorithms' benefits for the purpose of accuracy improvement and reliability of the digital text extraction procedure. OCR follows cutting-edge techniques to distinguish and identify individual characters, serving as the essential building block for text recognition. However, these algorithms may struggle when dealing with complex backdrops, blurry photos, or text that is structured in an atypical way. We use MSER approach, which excels at recognizing text sections by finding maximally stable regions across various severities and scales, to solve these constraints. The suggested model employs a multi-stage methodology. The MSER algorithm is used for extracting the likely text spots from the input image first. To boost OCR performance, these zones are then fine-tuned using pre-processing techniques including noise reduction and picture enhancement. The OCR system next processes the cleaned-up sections, identifying each region's text using machine learning and pattern recognition methods. The text that is recognized is then further processed to increase the accuracy and refine these findings. Thus, compared to most other models, the text recognition model that is built utilizing the MSER and the CNN (OCR, a component of CNN) algorithm performs better

A Machine Learning Approach for Air Writing Recognition

Abstract: Air Writing Recognition is a step towards the safety of people. Aim of this project is to make a step towards smart technology and provide an alternative when phones are unable to use. It helps the women, children and citizens in emergency situation. By writing a character (h, p) in air infront of CCTV, it recognizes that someone needs help. This project also send a voice message, text message to get emergency help and support. We hope that this project makes more safety to women, children and citizens and makes the crime rate less. The Air Writing Recognition project is a combination of computer vision object tracking and handwriting recognition. The Air Writing Recognition system uses the webcam of a computer to track character digits written in the air by the user, then uses a optical character recognition(OCR) algorithm to classify the character digits. And further it uses a Twilio account to make calls and sends message depends upon the character that the system recognized

Access to information research in the digital era

Access to information research in the digital era

Development of Automatic Inspection Systems for WRS2020 Plant Disaster Prevention Challenge Using Image Processing

In this article, an approach used for the inspection tasks in the WRS2020 Plant Disaster Prevention Challenge is explained. The tasks were categorized into three categories: reading pressure gauges, inspecting rust on a tank, and inspecting cracks in a tank. For reading pressure gauges, the “you only look once” algorithm was used to focus on a specific pressure gauge and check the pressure gauge range strings on the gauge using optical character recognition algorithm. Finally, a previously learned classifier was used to read the values shown in the gauge. For rust inspection, image processes were used to focus on a target plate that may be rusted for rust detection. In particular, it was necessary to report the rust area and distribution type. Thus, the pixel ratio and grouping of rust were used to count the rust. The approach for crack inspection was similar to that for rust. The target plate was focused on first, and then the length of the crack was measured using image processing. Its width was not measured but was calculated using the crack area and length. For each system developed to approach each task, the results of the preliminary experiment and those of WRS2020 are shown. Finally, the approaches are summarized, and planned future work is discussed.

A Privacy Preserving Generative Adversarial Network for Image Data

The extensive usage of online applications and social media has raised serious concerns from the public regarding the exposure of their personal information. So, there is a strong need for data anonymization to prevent privacy breaches and leakages. The era of attacks on databases and servers is an old trend. Now, most attacks are based on earning access to users’ private data. There are techniques like k-anonymity and l-diversity to protect Personally Identifiable Information (PII) from adversaries. However, these techniques still cannot provide security from homogeneity attacks, and their application is limited to structural data only. Till now, the frameworks are only available to anonymize the human face data in image format. In this paper, we proposed a new architecture for protecting privacy-related information in images of Indian vehicle number plates. We propose an architecture for anonymizing the vehicle number plates using Wasserstein’s Generative Adversarial Network (WGAN) by retaining the original data distribution even after anonymization. Our framework guarantees that it does not store any information while processing. Our main goal is to protect personal information from the image data. After anonymization, there is no similarity between the original and generated image. Our dataset includes a wide variety of license plates from all regions of India. Our work ensures that no human or a character recognition algorithm can recognize the characters from our anonymized images.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition. A detailed complexity study of the different blocks of this text recognition system has been realized. Following this study, an acceleration of the GCM algorithm (AGCM) is proposed. The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method. To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis. Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.

Deep learning Optical Character Recognition in PCB Dark Silk Recognition

For Automatic Optical Inspection (AOI) machines that were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated. Images captured by old AOI machines are not easy to be recognized by typical optical character recognition (OCR) algorithms, especially for dark silk. How to effectively increase silk recognition accuracy is indispensable for improving overall production efficiency in SMT plant. This paper uses fine tuned Character Region Awareness for Text Detection (CRAFT) method to build model for dark silk recognition. CRAFT model consists of a structure similar to U-net, followed by VGG based convolutional neural network. Continuous two-dimensional Gaussian distribution was used for the annotation of image segmentation. CRAFT model is good at recognizing different types of printed characters with high accuracy and transferability. Results show that with the help of CRAFT model, accuracy for OK board is 95% (error rate is 5%), and accuracy for NG board is 100% (omission rate is 0%).

Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition (OCR) refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image. This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence (AI) algorithms, in which the different AI algorithms for OCR analysis are classified and reviewed. Firstly, the mechanisms and characteristics of artificial neural network-based OCR are summarized. Secondly, this paper explores machine learning-based OCR, and draws the conclusion that the algorithms available for this form of OCR are still in their infancy, with low generalization and fixed recognition errors, albeit with better recognition effect and higher recognition accuracy. Finally, this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms. This paper concludes that OCR requires algorithms with higher recognition accuracy.

Raspbraille: Conversion to Braille Alphabet with Optical Character Recognition and Voice Recognition Algorithm

It is quite difficult for visually impaired individuals to read a physically printed material. It is a necessity to provide information flow and communication between visually impaired individuals and other individuals. For this purpose, an alphabet has been developed for visually impaired individuals. With this alphabet, which is called the Braille alphabet, it is possible for visually impaired individuals to access written culture. There are special letters in the alphabet designed for the visually impaired. With the development of technology, studies in the field of Braille alphabet have increased. Optical character recognition technology can convert the letters on files such as pdf, pictures, photos into understandable data by selecting and separating them. Speech recognition algorithm, computer, tablet, mobile phone, voice recorder devices, etc. It is the process of detecting and recognizing the human voice through a microphone with technological products. Within the scope of the study, a device has been developed for Braille alphabet by using optical character recognition technology and speech recognition algorithm. First, the images of each page of the books that were requested to be translated into Braille alphabet were taken. Then, a transformation was performed using a specific character set with the optical character recognition algorithm. Audiobooks, on the other hand, were first converted into text and then into Braille alphabet using speech recognition algorithm. With these converted characters, a physical hardware device has been developed to display Braille letters that visually impaired individuals can read faster.

Recognization Holic- Medicine Detection using Deep Learning Techniques

Everyone's life is significantly impacted by medicine. The rate at which a certain disease's treatment is discovered is rising daily. Therefore, it is important for people to be attentive when taking their medications and that they are knowledgeable about them. Providing various medication-related capabilities such as reminders to take medications on time, prescription information, and to aid chronic patients in taking numerous medications appropriately and avoiding taking the wrong medications, which may create drug interactions. The RECOGNIZATION HOLIC programme uses deep learning to create an intelligent system for identifying medications. By matching the text in the image with the dataset, which contains various drug names and their descriptions, the dataset can be displayed in this application. The scanned image is used to extract the text. By using an optical character recognition algorithm and tensor flow character training in the Android operating system, the text is recovered from the scanned image of the medication label. The reminder option with the medicine photo and the time to take their medicine is added into this application to help the people who might take the wrong medicine or forget to acquire the medicine. This software assists patients in taking their medications properly and at the ideal time.