Optical Character Recognition Algorithm Research Articles

License Plate Detection Based on Open CV

In this project a new approach of automatic vehicle number plate recognition in National Highways and this approach enables the vehicle to automatically pass the NH roads by using object detection, 3D environment construction, virtual line generation, path planning. The system is to monitor the vehicles that are entering and going out of the NH roads. All vehicles have their own unique license plate number, so the abstraction of plate number plays a major role in this system. The vehicle number plate is taken by the tool like machine learning based camera which is placed at the road junctions. The captured image will be processed by the automatic number plate recognition using OCR (optical character recognition) algorithm, here we using Gaussians filters for remove the blueness of the image using a Gaussian kernel suppresses only high-frequency spatial information. Then the entry and exit vehicle number should be recorded. A database is created with the vehicle number. This method is an efficient way of recognizing the vehicle number plate and strengthen the security system. In case of centralized receiver all the entrance records are kept in storage and it will check the presence of theft vehicle entering into the NH roads, it will be noticed and capture the vehicle image and vehicle information’s will be send to authorized person using IMAP (Internet Message Access Protocol). The OCR technique is used to identify the number. This system has many advantages like theft detection, less man power, user friendly, vehicle logging and less processing time.

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.

Image-Based Extraction of Prescription Information using OCR-Tesseract

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care.

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

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

Extracting laboratory test information from paper-based reports

BackgroundIn the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks.MethodsWe developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1).ResultsIn the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s.ConclusionIn this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline.

Advancements in OCR: A Deep Learning Algorithm for Enhanced Text Recognition

Optical Character Recognition (OCR) has significantly evolved with the rise of deep learning techniques. In this research paper, we present a novel and advanced OCR algorithm that leverages the power of deep learning for improved text recognition accuracy. Traditional OCR methods have faced limitations in handling complex layouts, noisy images, and diverse fonts, affecting overall performance. Our proposed algorithm addresses these challenges through the integration of deep neural networks, specifically convolutional and recurrent layers. The algorithm undergoes comprehensive training on large-scale datasets, enabling it to learn intricate patterns and features, resulting in robust recognition capabilities. Furthermore, we introduce an attention mechanism that enhances the model's ability to focus on critical text regions, enhancing accuracy and efficiency. Through extensive experiments and evaluations on benchmark datasets, we demonstrate the superiority of our deep learning-based OCR algorithm over conventional approaches. Our algorithm achieves state-of-the-art performance on various OCR tasks, including multilingual text recognition and document digitization. Additionally, we conduct an in-depth analysis of the algorithm's behaviour under various scenarios, such as low-resolution inputs and challenging environmental conditions. The findings from this research not only contribute to the field of OCR but also open avenues for applications in document analysis, text extraction, and content digitization in real-world scenarios. The integration of deep learning in OCR showcases its potential in revolutionising text recognition tasks, pushing the boundaries of accuracy and efficiency in this domain.

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

Text Recognition from Images using CNN and MSER Algorithms

Abstract: The ability to recognize text from images is a great importance in a range of applications, including document analysis, images captioning and augmented reality. The reliability and accuracy of text extraction from images have been completely transformed by text recognition models using Optical Character Recognition (OCR) and Maximally Stable Extremal Regions (MSER) algorithms. In our study, we propose a text recognition model that leverages the advantages of both OCR and MSER algorithms to enhance the reliability and accuracy of the text extraction process. OCR algorithm serve as the fundamental basis for text recognition by utilizing advanced methods to separate and identify individual characters. None the less these algorithms can encounter difficulties when confronted with intricated backgrounds, images of poor quality, or text arranged in irregular layouts. To address these limitations, we integrate the MSER algorithm, which excels in detecting text regions by identifying maximally stable regions across different scales and intensities. Our proposed model follows a multi-stage approach. First, the input image, the MSER method is used to extract probable text locations. These regions are then refined using pre-processing techniques, such as noise removal and image enhancement, to improve OCR performance. Next, the refined regions are passed through the OCR algorithm, which utilizes machine learning and pattern recognition techniques to recognize the text within each regions. The recognized text is subsequently post-processed to refine the results and improve overall accuracy. The text recognition model is implemented using CNN (OCR which is part of CNN) and the Maximally stable Extremal regions (MSER) algorithms

Realtime Detection of Table Objects and Text Areas for OCR Preprocessing

OCR (Optical Character Recognition) is a technology that automatically detects, recognizes, and digitally converts text into images. OCR has a variety of uses, including reducing human error when viewing and typing documents and helping people work more efficiently with documents. It can increase efficiency and save money by eliminating the need to manually type text, especially when scanning documents or digitizing images. OCR is divided into text object detection and text recognition in an image, and preprocessing techniques are used during the original document imaging process to increase the accuracy of OCR results. There are various preprocessing techniques. They are generally classified into image enhancement, binarization techniques, text alignment and correction, and segmentation techniques. In this paper, we propose a special-purpose preprocessing technique and application called Table Area Detection. Recently, table detection using deep learning has been actively researched, and the research results are helping to improve the performance of table recognition technology. Table detection will become an important preprocessing technology for text extraction and analysis in various documents, and it requires a lot of research and accuracy. While many previous studies have focused on improving the accuracy of OCR algorithms through various techniques, this study proposes a method to discover and exclude false positives by introducing a factor called Table Area Detection.

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.

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%).

Extracting Information from Vehicle Registration Plate using OCR Tesseract

The increase in population and vehicle ownership causes high levels of traffic density. The very high mobility of the population ultimately has an impact on routine congestion which is felt to be getting worse over time. There are several factors that cause traffic jams to get worse. One of them is due to the increasing number of motorized vehicles. The number of vehicles that continues to increase is not in accordance with the road capacity that can accommodate these cars. For this reason, a system is needed that can categorize cars that can run on certain days according to the number plate (eg odd or even) to reduce congestion. This built system can extract the license plates of vehicles passing on the highway using image processing and character recognition methods. This research paper proposed an Automatic Number Plate Recognition ANPR system is an image processing and character recognition system that is used to recognize a car's license plate using Optical Character Recognition (OCR). The inputted license plate is automatically localized, segmented, and recognized using the OCR algorithm provided in the Tesseract library. The experiment shows 83.3% accuracy due to the difference in license plate format, background, and fonts.

Multi-lingual character recognition and extraction using recurrent neural networks

In recent years, segmentation and recognition of multilingual languages have attracted the attention of many researchers. Multilingual Optical Character Recognition (OCR) technology uses tools like PyTesseract, OpenCV and Recurrent Neural Networks (RNN) to transform text in English, Telugu, Hindi, Tamil and Kannada. Converting text to digital format transforms communication and supports cultural understanding. The system supports multiple languages and can handle different languages. PyTesseract and OpenCV are used for accurate behavior recognition, while RNN improves language understanding. To ensure accuracy, the system uses advanced techniques to overcome problems such as noise and distortion in data input. This technology, combined with advanced OCR algorithms, improves text recognition and makes it adaptable to multilingual environments. This study highlights the importance of multilingual OCR in preserving language, supporting international cooperation, and encouraging participation in the digital age. The research explores ways to use cross-language grammar, fonts, and document layouts using previously implemented techniques to create informative content. RNN further improves the OCR process by capturing complex words. The userfriendly interface and integration with various platforms increase accessibility, allowing users to easily engage with multilingual content. Therefore, multilingual OCR, which combines PyTesseract, OpenCV, RNN, and other advanced techniques, is used to overcome speech problems, handle various grammars and input data, and have a positive impact on the development of OCR technology. This research helps create a globally connected society where knowledge is transmitted across language boundaries, fostering cultural exchange and fostering growth, while ensuring a good and accurate understanding of literature.

Optical Character Recognition Development Using Python

Optical Character Recognition (OCR) is a technology used to convert scanned or digital images into editable text. OCR has become an increasingly important tool in the fields of data extraction and information retrieval, allowing for quick and efficient conversion of scanned documents and digital images into text. In this paper, we explore the use of the Python programming language to implement OCR algorithms and systems. We provide a comprehensive overview of existing Python libraries and packages used for OCR, including Tesseract and pytesseract, along with their strengths and limitations. We also examine the different OCR approaches and techniques, including template matching, feature extraction, and encrypting/decrypting the OCR parsed files and discuss their implementation in Python. Finally, we present a case study of a simple OCR system built using Python and evaluate its performance on a sample dataset. The results of our study highlight the potential of Python for OCR implementation and demonstrate its feasibility for real-world applications.

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.