Optical Character Recognition Techniques Research Articles

Methodology for eliminating plain regions from captured images

Finding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities.

Electricity Theft Detection in Smart Grids Using Sarimax and OCR

This study proposes a novel approach to detect electricity theft in smart grids by combining SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Factors) models with Optical Character Recognition (OCR) techniques. Electricity theft remains a significant challenge in smart grid systems, leading to revenue losses and operational inefficiencies. Traditional methods often fall short in accurately identifying and addressing such illicit activities. The integration of SARIMAX models leverages time-series data, capturing patterns and anomalies indicative of electricity theft. These models incorporate exogenous factors such as weather conditions, economic indicators, and historical consumption patterns, enhancing the detection accuracy. Additionally, OCR technology is applied to analyze meter readings and identify discrepancies or irregularities that may signal potential theft instances. The synergy between SARIMAX and OCR offers a comprehensive solution for electricity theft detection in smart grids. By harnessing advanced analytics and machine learning algorithms, this approach enables utility providers to proactively identify suspicious activities, mitigate revenue losses, and ensure the integrity of their grid infrastructure

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.

INAPPROPRIATE CONTENT DETECTION IN IMAGE

The proliferation of social media platforms has led to a significant increase in the sharing of memes and other image-based content. However, some of these images may contain inappropriate or offensive text, which can be challenging to detect using traditional methods. Because the open-source existing solutions does not classify the text content in images. This project aims to develop a robust solution for detecting Not Safe for Work (NSFW) text content in images, with a particular focus on social media memes. The objectives of this project are to accurately extract text from images using optical character recognition (OCR) techniques, and to classify the extracted text as either NSFW or Safe for Work (SFW) using a fine-tuned natural language processing (NLP) model. The methodology employed in this project involves several key steps. First, the Keras OCR library is utilized to extract text from input images, as it has demonstrated superior performance compared to other OCR tools for social media images. Next, a preprocessing step is performed to align the extracted text in the correct sequence using coordinates, distance from the image origin, and the Pythagorean theorem. The pre-processed text is then passed to a fine-tuned BERT-BASE-UNCASED language model, which has been trained on a dataset of Reddit posts (NSFW and SFW) to classify the text as either NSFW or SFW. Key Words: OCR, Inappropriate, BERT, NSFW, SFW, Image, Text

Machine Learning Defenses: Exploring the integration of machine learning techniques within CAPTCHA systems to dynamically adjust challenge difficulty and thwart adversarial attacks

The utilization of machine learning (ML) techniques within CAPTCHA systems represents a significant advancement in cybersecurity, offering dynamic adaptability to thwart evolving adversarial attacks. This research delves into the integration of ML defenses within CAPTCHA frameworks, focusing on their efficacy in adjusting challenge difficulty dynamically to counter sophisticated attacks. By leveraging ML algorithms, CAPTCHA systems can analyze user behavior patterns and adaptively tailor challenges to deter automated bots while ensuring user accessibility and engagement. This paper aims to provide a comprehensive investigation into the design, implementation, and evaluation of ML-based CAPTCHA defenses. It explores various ML approaches, including supervised learning, reinforcement learning, and deep learning, in dynamically adjusting challenge complexity based on user interactions and environmental factors. Furthermore, the study delves into the assessment of ML-driven CAPTCHA systems' resilience against adversarial attacks, such as machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial image manipulation. Through empirical analysis and experimentation, this research endeavors to elucidate the effectiveness and limitations of ML-based CAPTCHA defenses in real-world scenarios. By elucidating the intricacies of integrating ML techniques within CAPTCHA systems, this paper seeks to contribute valuable insights to the field of cybersecurity, offering guidance for the development of robust and adaptive CAPTCHA mechanisms capable of mitigating emerging threats in the digital landscape.

Automatic Signature and Photo Detection from the Uploaded Document using AI and OCR

In our modern era, it is important for organizations in different industries to have efficient document processing capabilities. One key aspect is the identification of signatures and images in documents, which typically requires manual review. Fortunately, advancements in Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies present viable options for automating this task. This paper presents a novel method that utilizes AI and OCR techniques to automatically identify signatures and images in uploaded documents.The process starts with preparing documents and utilizing AI technology for recognizing signatures and photos. Optical Character Recognition (OCR) is also used for a thorough examination. This method enhances efficiency, precision, and adaptability in handling documents. It is applicable in various fields like finance, legal, and administration. The solution aims to transform document processing by providing a smooth and dependable way to detect signatures and photos in the modern age.

INVOICE DATA EXTRACTION USING OCR TECHNIQUE

Traditional invoice processing involves manual entry of data, leading to human errors, delays,and increased operational costs. The lack of automation results in inefficiencies, hindering organizations from promptly accessing critical financial information. This research addresses the pressing need for a reliable OCR-based solution to automate invoice data extraction, ultimately improving accuracy, reducing processing time, and enhancing overall business productivity. The project aims to automate invoice data extraction through Optical Character Recognition (OCR) techniques. Leveraging advanced image processing and machine learning, the system will analyze scanned or photographed invoices, extracting relevant information such as vendor details, itemized costs, and dates.This automation streamlines manual data entry processes, enhancing accuracy and efficiency in managing financial records. OCR invoicing is the process of training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. Keywords: Invoice, OCR, YOLO algorithm, Data Extraction, Image Processing, Database Integration.

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.

Knowledge-Enhanced Historical Document Segmentation and Recognition

Optical Character Recognition (OCR) of historical document images remains a challenging task because of the distorted input images, extensive number of uncommon characters, and the scarcity of labeled data, which impedes modern deep learning-based OCR techniques from achieving good recognition accuracy. Meanwhile, there exists a substantial amount of expert knowledge that can be utilized in this task. However, such knowledge is usually complicated and could only be accurately expressed with formal languages such as first-order logic (FOL), which is difficult to be directly integrated into deep learning models. This paper proposes KESAR, a novel Knowledge-Enhanced Document Segmentation And Recognition method for historical document images based on the Abductive Learning (ABL) framework. The segmentation and recognition models are enhanced by incorporating background knowledge for character extraction and prediction, followed by an efficient joint optimization of both models. We validate the effectiveness of KESAR on historical document datasets. The experimental results demonstrate that our method can simultaneously utilize knowledge-driven reasoning and data-driven learning, which outperforms the current state-of-the-art methods.

TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models

In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. Finally, we perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.

Optimizing Prioritization of Crowdsourced Test Reports of Web Applications through Image-to-Text Conversion

Collating vast test reports is a time-consuming and laborious task in crowdsourced testing. Crowdsourced test reports are usually presented in two ways, one as text and the other as images, which have symmetrical content. Researchers have proposed many text- and image-based methods for prioritizing crowdsourced test reports of mobile applications. However, crowdsourced test reports of web applications typically have clearer textual descriptions of errors but noisier screenshots of errors. This gap motivates us to propose a method for prioritizing crowdsourced test reports of web applications to detect all errors earlier. In this paper, we integrate text and image information from test reports to enhance the analysis process. First, we use the natural language processing (NLP) technique to extract textual features of error descriptions and then symmetrically extract image features of error screenshots, i.e., we use the optical character recognition (OCR) technique to obtain textual information in the screenshots and then also use the NLP technique to extract features. To validate our approach, we conduct experiments on 717 test reports. The experimental results show that our method has a higher APFD (average percentage fault detection) and shorter runtime than state-of-the-art prioritization methods.

Design and Implementation of An Automated Car Plate Number Recognition System (ACPNRS)

The escalating global volume of vehicles on roadways necessitates innovative solutions for efficient traffic management, heightened security, and improved law enforcement. This research project centers on developing an Automated Car Plate Recognition System (ACPRS) to address these challenges. The ACPRS utilizes cutting-edge technologies in image processing, machine learning, and database integration for accurate and real-time license plate recognition. Beginning with an exploration of existing methodologies and technologies in the field, the research highlights their strengths and limitations. The conceptualization phase involves meticulous design, incorporating image preprocessing, license plate detection, character segmentation, optical character recognition (OCR), and database interaction. The research emphasizes the utilization of advanced image processing techniques, including deep learning algorithms, for robust license plate detection. Spectral analysis and character segmentation ensure reliable recognition in challenging conditions. OCR techniques enhance alphanumeric character interpretation, contributing to overall efficacy. The implementation phase involves software development, database setup, and integration with mobile applications for enhanced accessibility. Real-world applicability and scalability are pivotal considerations. The ACPRS is deployable in various contexts, such as parking management, security systems, and access control. Compatibility with mobile applications ensures widespread accessibility, broadening potential applications in diverse settings. Security and privacy measures include user authentication and data encryption to safeguard sensitive information. To validate the ACPRS’s effectiveness, comprehensive testing and evaluation use a diverse dataset. Real-world images, synthetic data, and publicly available datasets assess performance under different conditions. Evaluation metrics such as accuracy, speed, and robustness quantify capabilities and identify areas for improvement. In conclusion, this research project represents a significant contribution to automated vehicle identification. The proposed ACPRS, focusing on accuracy, real-time processing, and mobile accessibility, addresses critical challenges in traffic management, security, and law enforcement. Thorough exploration, design, implementation, and evaluation phases ensure understanding of the system’s capabilities and potential real-world applications. This work will be very significant to the developing country in managing their traffic and other related issues.

Smart Eye for Visually Impaired People

Abstract: This paper presents a novel mobile application tailored to enhance the daily experiences of individuals with visual impairments. Leveraging cutting-edge real-time object detection and Optical Character Recognition (OCR) techniques, the app, constructed using the TensorFlow algorithm and the comprehensive Common Objects in Context (COCO) dataset, aims to empower users by providing invaluable assistance through their mobile devices. The application utilizes the device's camera to seamlessly identify objects in real-time via the object detection algorithm, while simultaneously extracting and vocalizing text from captured images through OCR technology. A crucial aspect of the app lies in its commitment to user-friendliness, ensuring a straightforward interface tailored specifically for the needs of visually impaired individuals. The efficacy of the system was rigorously evaluated, focusing on both accuracy and real-time performance. Results conclusively demonstrate the app's effectiveness and efficiency in offering essential support to visually impaired users, marking a significant stride toward fostering independence and ease in their daily lives. Remarkably, this mobile application stands out for its affordability and accessibility, providing a practical and inclusive solution for individuals with visual impairments. By delivering audio feedback about identified objects and recognized text, this innovative app contributes to a more accessible and autonomous lifestyle for its users, contributing positively to the broader landscape of assistive technology for the visually impaired.

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.

Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition

License plate recognition plays a central role in a variety of application contexts, including traffic management, automated parking, and law enforcement. Among the various approaches available, the Optical Character Recognition (OCR) technique has proven its effectiveness in recognizing characters in license plate images. This study describes an approach for detecting and recognizing vehicle license plates by utilizing the OCR method with Bounding Box, Thresholding, and template matching. In addition, this study uses MATLAB R2022a software as the main tool in developing and implementing the method. The goal is to recognize vehicle license plates from images, describe their characteristics, and generate relevant information. This approach involves a series of image processing steps starting with the pre-processing stage, followed by the process of binarization and license plate segmentation. After successfully isolating the license plate area, isolating the character using a bounding box is performed using image separation techniques. The OCR method is used to recognize license plate characters through comparison using the correlation method. Through a series of experiments on several image datasets, this approach succeeded in showing that out of 20 sampled license plate images, the results obtained were a reading accuracy of 93.55% of 100%, recognizing 13 out of 20 license plate images accurately when tested. Thus, the findings of this research are expected to contribute to the recognition of vehicle license plates that are accurate and efficient, by utilizing image processing techniques and OCR methods implemented using MATLAB R2022a software.

COMPARATIVE STUDY OF FONT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS AND TWO FEATURE EXTRACTION METHODS WITH SUPPORT VECTOR MACHINE

Font recognition is one of the essential issues in document recognition and analysis, and is frequently a complex and time-consuming process. Many techniques of optical character recognition (OCR) have been suggested and some of them have been marketed, however, a few of these techniques considered font recognition. The issue of OCR is that it saves copies of documents to make them searchable, but the documents stop having the original appearance. To solve this problem, this paper presents a system for recognizing three and six English fonts from character images using Convolution Neural Network (CNN), and then compare the results of proposed system with the two studies. The first study used NCM features and SVM as a classification method, and the second study used DP features and SVM as classification method. The data of this study were taken from Al-Khaffaf dataset [21]. The two types of datasets have been used: the first type is about 27,620 sample for the three fonts classification and the second type is about 72,983 sample for the six fonts classification and both datasets are English character images in gray scale format with 8 bits. The results showed that CNN achieved the highest recognition rate in the proposed system compared with the two studies reached 99.75% and 98.329 % for the three and six fonts recognition, respectively. In addition, CNN got the least time required for creating model about 6 minutes and 23- 24 minutes for three and six fonts recognition, respectively. Based on the results, we can conclude that CNN technique is the best and most accurate model for recognizing fonts.

English text recognition technology and application analysis based on optical character recognition

With the rapid development of artificial intelligence, it is gradually applied to more and more fields. Ais market and technological potential value are unpredictable, which involves interaction between Ai and humans. In addition, optical character recognition is one of the essential roles in the interaction between machines and humans. Recognition of human language from a machine can significantly increase the user's experience and potential efficiency. Hence, this paper will focus on analyzing todays Optical character recognition (OCR) techniques based on feature extraction and neural networks. This article first provides an overview of the important significance of English text recognition, and then selects two typical methods for overview. Then, comparative experiments are conducted on the above methods and their analysis is conducted. By comparing each technique, to decide the restriction and advantage, based on the analysis to conclude and make predictions in OCR optimization.

Number Plate Detection in an Image

Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this area. We propose a unified convolutional neural network (CNN) and the F1 score as metrics in a deep learning project for picture categorization which can localize license plates and recognize the letters. We work on license plate recognition and segments characters in the license plate firstly, and then recognizes each segmented character using Optical Character Recognition(OCR)techniques. Extensive experiments show the effectiveness and the efficiency of our proposed approach.

Recognition of Hindi Character Using OCR-Technology: A Review

Recognition of character is a technique that enables the transformation of various kinds of scanned papers into an editable, readable, and searchable format. In the last two decades, several researchers and technologists have been continuously working in this field to enhance the rate of accuracy. Recognition of character is classified into printed, hand-written, and characters written at image recognition. Recognition of character is the major area of research in the field of pattern recognition. This paper presents an overview of Hindi character recognition by utilizing the optical character recognition (OCR) technique. We surveyed some major research breakthroughs in character recognition, especially for Hindi characters. This research article focuses to provide a deeper insight into the researchers and technologists working in the field of recognition of Hindi-character.

VERIFIKASI DOKUMEN TRANSKRIP NILAI SEMESTER MENGGUNAKAN METODA OPTICAL CHARACTER RECOGNITION

At the Ambon State Polytechnic, students' semester grade reports are still manually typed. This causes frequent typo errors which can result in the invalidity of the document, let alone incorrect grades, student identification numbers and many other label values. Here a java application has been implemented to detect these errors. This application is primarily intended for officials of the Head of Study Program, Head of the Department before signing and validating the report. Officials who legalize it will be greatly assisted because tedious validation work can be replaced by computers. The validation process is carried out by utilizing the optical character recognition technique from the open source library Tesseract-OCR. From the experimental results the verification process can be improved by using OCR specific on specific regions of interest (ROI) after using template matching method from OpenCV. The consideration of the Levehnstein distance in the comparison of label values against the reference database also improves the success rate of the algorithm. The database used has been tested for about 800 grade report documents, with successful verification result above 90%.