OCR Techniques Research Articles

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

A Comprehensive Survey of Advanced Image Processing and OCR Techniques for Enhanced Image Plagiarism Detection

In today's digital world, sharing files, documents, and presentations online has become routine for both work and study purposes. However, this surge in online sharing has brought forth a significant challenge: plagiarism. Plagiarism will happen whenever the copied content in various sources by not giving proper credit. This paper delves into the realm of plagiarism detection, particularly focusing on images. We discuss various tools and techniques designed to detect plagiarism and compare their effectiveness based on factors like accuracy. Finally, we analyze the work of different authors and share common findings. Our research employs a thorough review process to ensure the accuracy of our conclusions. By emphasizing the use of AI-powered tools, our agenda is to promote sharing of original dataover the online domain.

Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR

One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.

OCR 및 Average Hash 기법을 사용한 이미지 기반 유해사이트 판별 방법

Recently, websites containing harmful information such as gambling, illegal drugs, pornography, and prostitution are exposed to the public. These harmful sites cause damage to copyright holders and related service industries, and cause various social problems. In this paper, we propose an image-based harmful site identification system using OCR and Average Hash techniques to identify and classify harmful sites. This system uses the characteristic that most gambling banner advertisements repeatedly use similar images, and analyzes the similarity with the average hash value of the banner advertisement image. And using Easy OCR, it determines whether the phrase written in the banner advertisement is harmful or not. To evaluate the performance of the proposed idea, a program was created to determine harmfulness by collecting and analyzing the site"s banner advertisement image when the site name was entered, and it was confirmed that the discrimination accuracy was 84%. In addition, since the information collected while running the program is stored in the database, trends in harmful sites can be identified. This will be effectively used to search for harmful sites that are expected to occur.

Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing.

Web Application for Interpretation of Doctor's Handwritten Prescription and Suggesting the best Price Offer over Various e-Commerce Websites using AI

Patient record-keeping is crucial for accurate diagnoses and treatment. In India, due to time constraints, most doctors manually write prescriptions, making it challenging for pharmacists to read them correctly. This increases the risk of dispensing the wrong medicine, which can have serious and even fatal consequences for patients. To address this problem, this research proposes an online handwritten medical prescription recognition system that allows users to scan prescriptions and compare prices across different websites. OCR techniques are used to recognize medicine names in handwritten prescriptions, while NLP techniques such as Cosine Similarity are employed to overcome the issue of misinterpretation. The review study focuses on Named Entity Recognition and Relation Extraction, which help identify named entities and extract relations between entities. Most state-of-the-art techniques are offline and computationally expensive, highlighting the need for understanding the deep-learning processes used in the proposed system. The system stores various features such as medicine names, uploaded images, user details, and search history to improve recognition accuracy. Additionally, new features are proposed to enhance accuracy further. Uses a local dataset from a pharmacy shop and past prescription records from local doctors.

Library on-shelf book segmentation and recognition based on deep visual features

On-shelf book segmentation and recognition are crucial steps in library inventory management and daily operation. In this paper, a detailed investigation of related work is conducted. RFID and barcode-based solutions suffer from expensive hardware facilities and long-term maintenance. Digital Image processing and OCR techniques are flawed due to a lack of accuracy and robustness. On this basis, we propose a visual and non-character system utilizing deep learning methods to accomplish on-shelf book segmentation and recognition tasks. Firstly, book spine masks are extracted from the image of on-shelf books by instance segmentation model, followed by affine transformation to rectangle images. Secondly, a spine feature encoder is trained to learn the deep visual features of spine images. Finally, the book inventory search space is constructed and the similarity metric between spine visual representations is calculated to recognize the target book identity. To train the models we collect high-resolution datasets of 10k-level and develop a data annotation software accordingly. For validation, we design simulated scenarios of recognizing 3.6k IDs from 5.6k book spines and achieve a best top1 accuracy of 99.18% and top5 accuracy of 99.91%. Furthermore, we develop a prototype of a mobile library management robot with embedded edge intelligence. It can automatically perform on-shelf book image capturing, spine segmentation and recognition, and target book grasping workflow.

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach.

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view.

Friction and maximum dilatancy angles of granular soils incorporating low plastic fines and depositional techniques effects

This paper addresses the effects of over consolidation ratio (OCR = 1–8), low plastic fines content (Fc = 0–40%) and depositional techniques (DFP and WD) on the stress-strain and volume change behaviours as well as maximum dilatancy, peak-state friction, critical state friction and excess friction angles through two series of drained triaxial compression tests. The first one, emphasizing the effect of the OCR and the second one analysing the influence of Fc on the mechanical behaviour of Chlef sand and Chlef sand-silt mixtures, respectively. All specimens were prepared at a medium density (Dr = 53%) and subjected to a confining pressure (σ′3 = 0.1 MPa). The obtained results indicate that the increase in the fines content of the DFP specimens leads to a decrease in the deviatoric stress and dilative character at the same axial strain, however, the reverse trend is observed for the WD ones. Moreover, it is observed that the deviatoric stress generally increases with the increasing of the OCR for both depositional techniques and the DFP specimens prepared exhibit higher values of the shear strength than the WD ones. Furthermore, the results show that Fc, OCR and depositional techniques influence significantly the mechanical characteristics (ϕ′ps, ϕ′cs, ψmax and ϕ′ex).

A Review Paper on License Plate Recognition System

ANPR provides solution in which the steps to run an efficient intelligent transport network might be taken. Owing to the rapid increase in vehicles it has become a requirement for traffic control management. ANPR's main goal is to track traffic and for the purpose of defense. Number plate recognition uses image processing techniques or OCR techniques and edge detection technology to detect characters on license plates. The model comprises of three modules which are module for car detection, module for license plate segmentation and module for recognition. Starting from auto robberies, violating traffic laws, to law enforcement administration, Image processing gave us a determination to put a stop to these violations. This review paper provided an examination of the different license plate recognition design implemented so far.

A Workflow for Data Extraction from Digitized Herbarium Specimens

Based on own work on species and trait recognition and complementary studies from other working groups, we present a workflow for data extraction from digitized herbarium specimens using convolutional neural networks. Digitized herbarium sheets contain: preserved plant material as well as additional objects: the label containing information on the collection event, annotations such as revision labels, or notes on material extraction, identifiers such as barcodes or numbers, envelopes for loose plant material and often scale bars and color charts used in the digitization process. preserved plant material as well as additional objects: the label containing information on the collection event, annotations such as revision labels, or notes on material extraction, identifiers such as barcodes or numbers, envelopes for loose plant material and often scale bars and color charts used in the digitization process. In order to treat these objects appropriately, segmentation techniques (Triki et al. 2018) will be applied to localize and identify the different kinds of objects for specific treatments. Detecting presence of plant organs such as leaves, flowers or fruits is already a first step in data extraction potentially useful for phenological studies. Plant organs will be subject to routines for quantitative (Gaikwad et al. 2018) and qualitative (Younis et al. 2018) trait recognition routines. Text-based objects can be treated as described by Kirchhoff et al. 2018, using OCR techniques and considering the many collection-specific terms and abbreviations as described in Schröder 2019. Additionally, species recognition (Younis et al. 2018) will be applied in order to help further identification of incompletely identified collection items or to detect possible misidentifications. All steps described above need sufficient training data including labelling that may be obtained from collection metadata and trait databases. In order to deal with new incoming digitized collections, unseen data or categories, we propose implementation of a new Deep Learning approach, so-called Lifelong Learning: Past knowledge of the network is dynamically saved in latent space using autoencoder and generatively replayed while the network is trained on new tasks which enables it to solve complex image processing tasks without forgetting former knowledge while incrementally learning new classes and knowledge.

A Survey on Easy OCR Techniques used to build Systems for Visually Impaired People

A Survey on Easy OCR Techniques used to build Systems for Visually Impaired People

Gathering, Classifying and Visualizing Results from Social Surveys using OCR and Machine Learning Techniques

Gathering, Classifying and Visualizing Results from Social Surveys using OCR and Machine Learning Techniques

Review of Optical Character Recognition Techniques& Applications

The Character Recognition of both keyboard typed and handwritten characters has still a long way to go in terms of research. Although significant success has been achieved in type written characters but in handwritten it is still to touch an appreciable level. Most of the methods that have been proposed in this regard have huge computational complexity. The proposed review provides an in depth review of the OCR methods which include segmentation, classification and recognition of characters independent in size and texture. The proposed review also provides the literature survey in a summarized manner providing a comparative analysis of various OCR techniques.

Offline Urdu OCR using Ligature based Segmentation for Nastaliq Script

There are two most popular writing styles of Urdu i.e. Naskh and Nastaliq. Considering Arabic OCR research, ample amount of work has been done on Naskh writing style; focusing on Urdu, which uses Arabic character set commonly used Nastaliq writing style. Due to Nastaliq writing style, Urdu OCR poses many distinct challenges like compactness, diagonal orientation and context character shape sensitivity etc., for OCR system to correctly recognize the Urdu text image. Due to compactness and slanting nature of Nastaliq writing style, existing methods for Naskh style would not give desirable results. Therefore, in this paper, we are presenting ligature based segmentation OCR system for Urdu Nastaliq script. We have discussed in detail various unique challenges for the Urdu OCR and different feature extraction techniques for Ligature recognition using SVM and kNN classifier. The system is trained to recognize 11,000 Urdu ligatures. We have achieved overall 90.29% accuracy tested on Urdu text images.

Sanskrit Character Recognition System Using Neural Network

In the fast moving world with the amazingly growing technology, character recognitions play a wide role by providing more scope to perform research in OCR techniques. Sanskrit handwritten recognition has been one of the challenging research areas in the field of pattern recognition. Character recognition is the electronic translation of scanned images of handwritten or printed text into a machine encoded text. The character recognition is a standout amongst the most generally utilized biometric attributes for authentication of persons and document. In this paper proposed an off line handwritten character recognition framework utilizing feed forward neural network. A handwritten Sanskrit character is resized into 20x30 pixels and this character is used for training the neural network. After the training process, the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Sanskrit characters has been calculated and compared. The results of the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems. Keywords: Classification, Feed Forward Neural Network, Handwritten Sanskrit Character Recognition, Image Extraction, Pre-Processing

Degraded Script Identification for Indian Language- A Survey

The working module of any Optical character Recognition system almost depends upon printing and paper of the input document image. A number of OCR techniques are available and claim correctly identified accuracy in printed document image in Indian and foreign script. A few report have been found on the recognition of the degraded Indian language document. The degradation in any scanned printed document can be of many types. In this paper, we focus a survey of degraded script identification for Indian Language document.

A Novel Model-based Dewarping Technique for Advanced Digital Library Systems

Digitization often introduces distortions in the form of odd perspective and curved text lines, especially toward the spine region of bound documents, that tamper both the creation of an acceptable digital reproduction of the document and the successful extraction of its textual content using OCR techniques. While already known for traditional acquisition means such as flatbed or planetary scanners, the problem gets even worse with the use of cameras, whose current widespread availability may open new opportunities for librarians and archivists. Dewarping is in charge of handling this kind of problems. This paper proposes a novel model-based dewarping method, aimed at solving some of the shortcomings of existing approaches through the use of a mixture of image processing and numerical analysis tools. The method is based on the construction of a curvilinear grid on each page of the document by means of piecewise polynomial fit and appropriate equipartition of a selection of its curved text lines. We show the method on sample documents and evaluate its impact on successful rate of OCR on a dataset.

Review of OCR Techniques Used in Automatic Mail Sorting of Postal Envelopes

This paper presents a review of various OCR techniques used in the automatic mail sorting process. A complete description on various existing methods for address block extraction and digit recognition that were used in the literature is discussed. The objective of this study is to provide a complete overview about the methods and techniques used by many researchers for automating the mail sorting process in postal service in various countries. The significance of Zip code or Pincode recognition is discussed.

An approach to offline handwritten Devanagari word segmentation

Hindi is very popular language after Mandarin and English. Its script is Devanagari. Although major work is reported on OCR techniques for machine printed Devanagari script, very few works are beginning to report on offline handwritten Devanagari OCR. Particularly, very few works have been reported so far on segmentation of Devanagari words. This paper paves a step in that direction and open avenues for researchers in that field. A novel segmentation approach is proposed for segmentation of offline handwritten Devanagari words using techniques of Hough transform and connected components. The difficulties for segmentation in Devanagari script and systematic steps to accommodate those difficulties as much as possible have been presented with elaborate results.