Optical Character Recognition System Research Articles

Handwritten Optical Character Recognition to Digital Text Conversion

Handwritten Optical Character Recognition (OCR) is a crucial technology for converting handwritten text into digital format. This process involves detecting and interpreting handwritten characters from images or scanned documents. In this project, we focus on OCR for three languages: Kannada, Telugu, and Hindi. The system utilizes machine learning algorithms, specifically trained neural networks, to recognize and transcribe handwritten characters accurately. The OCR system preprocesses the input images, applies character segmentation, and then classifies each segment into the corresponding character class. Post- processing techniques may be applied to improve accuracy and handle noise. The converted digital text can then be further processed, analyzed, or stored as needed. This technology has various applications in digitizing historical documents, automating data entry tasks, and enabling accessibility for visually impaired individuals

Interpreting Doctor Notes using Handwriting Recognition

Abstract: Handwriting recognition of medical prescriptions has been a challenging problem over the recent years with constant research in providing possible accurate solutions. Indecipherable handwritten prescription and inefficiency of Pharmacist to understand the medical prescription can lead to serious and harmful effect to the patients. Even in the recognition of handwriting, mainly doctors notes, they are very difficult for everyone to understand and it takes time for a person to analyse it. So, this idea mainly focused on interpreting doctor’s notes using handwritten recognition and deep learning techniques. The handwritten or printed document pictures are transformed into their electronic counterparts using an optical character recognition (OCR) system. Due to individuals' inconsistent writing styles, dealing with handwritten texts is significantly more difficult than dealing with printed ones. Handwritten text recognition could be done by Image processing, Machine Learning or Deep Learning Techniques. Out of these Deep Learning remains to be the most popular and prominent. Some of the Deep Learning techniques includes Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This gives a review of the various recognition methodologies used for interpreting handwritten texts. It includes the most important algorithms that could be used for detecting the handwritten word/text/character by using various approaches for the recognition process. In the end we are thus comparing the accuracies provided by these systems.

Adapting multilingual vision language transformers for low-resource Urdu optical character recognition (OCR).

In the realm of digitizing written content, the challenges posed by low-resource languages are noteworthy. These languages, often lacking in comprehensive linguistic resources, require specialized attention to develop robust systems for accurate optical character recognition (OCR). This article addresses the significance of focusing on such languages and introduces ViLanOCR, an innovative bilingual OCR system tailored for Urdu and English. Unlike existing systems, which struggle with the intricacies of low-resource languages, ViLanOCR leverages advanced multilingual transformer-based language models to achieve superior performances. The proposed approach is evaluated using the character error rate (CER) metric and achieves state-of-the-art results on the Urdu UHWR dataset, with a CER of 1.1%. The experimental results demonstrate the effectiveness of the proposed approach, surpassing state of the-art baselines in Urdu handwriting digitization.

Optical Character Recognition System in Healthcare and Hospital Management

The paper proposes a new Optical Character Recognition (OCR) model for medical documents. This model uses advanced machine learning and deep learning to improve the accuracy of extracting text from scanned medical images and various other documents. The goal is to make medical records more accessible and easier to manage electronically. The model can handle both English and multiple other languages, making it suitable for global healthcare use. It also explores adding Text-to-Speech functionality to convert the extracted text to audio, improving accessibility for users with visual impairments. By combining these features with insights from previous OCR and Text-to-Speech research, the model aims to streamline healthcare workflows and improve accessibility for everyone.

Survey on text-line segmentation process in historical Arab manuscripts

The segmentation process entails dividing or decomposing the entire document image into segments or lines. This technique serves as a fundamental step in developing any writing or optical character recognition system. However, numerous existing segmentation schemes encounter challenges when dealing with specific script styles, like ancient or historical Arabic writing found in ancient manuscripts. which possesses unique characteristics. These characteristics include inclined text lines, overlapping letters, diacritic marks, decorative elements, variable letter forms, and ligatures (combinations of two or more letters merged to form a single connected shape).Thus, in this paper we present a thorough survey of the field. The survey is composed of two segments. The first segment provides a concise overview of the historical Arabic documents. The second, which serves as the primary segment, focuses on the crucial step of handwritten document recognition, specifically segmentation. A detailed and systematic overview of the various approaches to segmentation, including different levels, employed for extracting handwritten Arabic text-lines, is outlined. Subsequently, a literature study is conducted to review and analyze proposed works in this area.

Empowering Mathematics Education in Greek Primary Schools: Bridging the Divide through ICT Integration and Interactive Boards for Enhanced Teacher ICT Literacy

This article advocates for a tech-enriched teaching approach using Information and Communication Technologies (ICT) and interactive boards to enhance students’ understanding and problem-solving skills. Overcoming challenges requires systematic planning, progressive policies, dedicated funds, and regular teacher training. The essay also discusses the benefits and limitations of ICT, the key principles of interactive whiteboard application in pedagogy, and the dynamic challenges faced by Greek elementary schools in redesigning mathematics pedagogy with ICT. It proposes blending modern technologies with traditional instructions, emphasizing the need for continuous teacher development and a positive attitude toward technology. The way forward involves leveraging Optical Character Recognition (OCR) systems and interactive boards, necessitating strategic planning, training programs, and infrastructure investment for a tech-enriched teaching future in Greek primary schools.

Reading between the Lines: Image-Based Order Detection in OCR for Chinese Historical Documents

Chinese historical documents, with their unique layouts and reading patterns, pose significant challenges for traditional Optical Character Recognition (OCR) systems. This paper introduces a tailored OCR system designed to address these complexities, particularly emphasizing the crucial aspect of Reading Order Detection(ROD). Our system operates through a threefold process: text detection using the Differential Binarization++ model, text recognition with the SVTR Net, and a novel ROD approach harnessing raw image features. This innovative method for ROD, inspired by human perception, utilizes visual cues present in raw images to deduce the inherent sequence of ancient texts. Preliminary results show promising reductions in page error rates. By preserving both content and context, our system contributes meaningfully to the accurate and contextual digitization of Chinese historical manuscripts.

The Industrial Application of Artificial Intelligence-Based Optical Character Recognition in Modern Manufacturing Innovations

This paper presents the development of a comprehensive, on-site industrial Optical Character Recognition (OCR) system tailored for reading text on iron plates. Initially, the system utilizes a text region detection network to identify the text area, enabling camera adjustments along the x and y axes and zoom enhancements for clearer text imagery. Subsequently, the detected text region undergoes line-by-line division through a text segmentation network. Each line is then transformed into rectangular patches for character recognition by the text recognition network, comprising a vision-based text recognition model and a language network. The vision network performs preliminary recognition, followed by refinement through the language model. The OCR results are then converted into digital characters and recorded in the iron plate registration system. This paper’s contributions are threefold: (1) the design of a comprehensive, on-site industrial OCR system for autonomous registration of iron plates; (2) the development of a realistic synthetic image generation strategy and a robust data augmentation strategy to address data scarcity; and (3) demonstrated impressive experimental results, indicating potential for on-site industrial applications. The designed autonomous system enhances iron plate registration efficiency and significantly reduces factory time and labor costs.

Optically processed Kannada script realization with Siamese neural network model

<p><span>Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.</span></p>

A study of feature extraction for Arabic calligraphy characters recognition

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.

Development of novel optical character recognition system to reduce recording time for vital signs and prescriptions: A simulation-based study.

Digital advancements can reduce the burden of recording clinical information. This intra-subject experimental study compared the time and error rates for recording vital signs and prescriptions between an optical character reader (OCR) and manual typing. This study was conducted at three community hospitals and two fire departments in Japan. Thirty-eight volunteers (15 paramedics, 10 nurses, and 13 physicians) participated in the study. We prepared six sample pictures: three ambulance monitors for vital signs (normal, abnormal, and shock) and three pharmacy notebooks that provided prescriptions (two, four, or six medications). The participants recorded the data for each picture using an OCR or by manually typing on a smartphone. The outcomes were recording time and error rate defined as the number of characters with omissions or misrecognitions/misspellings of the total number of characters. Data were analyzed using paired Wilcoxon signed-rank sum and McNemar's tests. The recording times for vital signs were similar between groups (normal state, 21 s [interquartile range (IQR), 17-26 s] for OCR vs. 23 s [IQR, 18-31 s] for manual typing). In contrast, prescription recording was faster with the OCR (e.g., six-medication list, 18 s [IQR, 14-21 s] for OCR vs. 144 s [IQR, 112-187 s] for manual typing). The OCR had fewer errors than manual typing for both vital signs and prescriptions (0/1056 [0%] vs. 14/1056 [1.32%]; p<0.001 and 30/4814 [0.62%] vs. 53/4814 [1.10%], respectively). In conclusion, the developed OCR reduced the recording time for prescriptions but not vital signs. The OCR showed lower error rates than manual typing for both vital signs and prescription data.

Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection

AbstractIn recent years, automatic document and text analysis has gained significant importance, driven by advancements in optical character recognition (OCR) technology and the need for efficient processing of large volumes of printed or handwritten documents. This article specifically focuses on document layout analysis (DLA) and text line detection (TLD), both of which are crucial components of OCR systems. Our objective is to develop an effective method for extracting both textual and non‐textual regions, addressing challenges unique to the Persian (and Persian‐like) language(s). In the DLA stage, we employ deep learning models and a voting system to accurately determine the regions of interest. Additionally, we introduce methods such as optimum font size concepts, angle correction, and a line curvature elimination algorithm in the TLD process to enhance OCR accuracy. Comparative evaluations against state‐of‐the‐art methods demonstrate the superiority of our approach, showcasing a 2.8% improvement in the accuracy of Tesseract‐OCR 5.1.0 (a well‐established commercial OCR system) on the official Iranian newspapers dataset. These findings underscore the importance of addressing DLA and TLD challenges to advance OCR technology for Persian language documents and provide a solid foundation for future research in this domain.

Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods

In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual understanding, domain-specific adaptation, robustness and noise tolerance, and scalability and extensibility. The approach aims to leverage techniques like convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, and single visual models for scene text recognition. While focusing on English, Hungarian, and Japanese as representative languages, the proposed methodology can be extended to any existing or even synthesized languages. The development of accurate, efficient, and versatile OCR systems is at the core of this research, offering societal benefits by bridging global communication gaps, ensuring reliability in diverse environments, and demonstrating the adaptability of AI to evolving needs. This work not only mirrors the state of the art in the field but also paves new paths for future innovation, accentuating the importance of sustained research in advancing AI’s potential to shape societal development.

Design and development of OCR software for remote measurement and calibration

The advent of digital technology has led to significant changes in work processes in many organizations and industries worldwide including Thailand. This work aims to apply digital technology to improve measurement and metrology in terms of accuracy, productivity, and variability. We developed machine vision software for monitoring and collecting digital data from measurement systems. The application of Optical Character Recognition (OCR) technology in metrology is the first step toward digitalization, enabling enterprises to improve their measurement processes by automating data collection, analysis, and management. Moreover, it can be integrated with machine learning algorithms to enhance the automation of complex measurement tasks. To achieve this goal, an OCR software for data recording was designed in the LabVIEW environment. The image captured by the camera is processed by the LabVIEW OCR system which is trained continuously until it can recognize characters consistently and accurately. This software has been tested with the calibration systems which has a seven-segment digital format. The result shows that the software can acquire and transmit the instrument reading with reliable and exceptional accuracy. In addition, it can also reduce the steps and time required for measurement. In conclusion, the application of OCR technology can improve the efficiency, productivity, and accuracy of measurement. It also has a promising potential to enable remote measurement and calibration according to ISO/IEC 17025 possible.

A Deep Learning Based Offline Optical Character Recognition Model for Printed Ottoman Turkish

Developing efficient optical character recognition (OCR) systems for printed Ottoman text is a problem since current OCR models created for Arabic have restrictions that make it difficult to be performed. The performance of these models has been shown to be low when used for the recognition of Ottoman text. It has also been shown that these models that have been subjected to specialized training on Ottoman text have produced results that are not sufficient. In this study, an analysis of printed Ottoman Turkish documents in the Matbu font is conducted using a deep learning model that is proposed. Through the use of an end-to-end trainable architecture that integrates convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) units, this study proposes an efficient solution to the Ottoman optical character recognition (OCR) issue. Experimental results show that the proposed model achieved overall scores for accuracy, sensitivity, and precision of 99.6%, 87.1%, and 93.3% on the test dataset respectively.

The Future of Natural History Transcription: Navigating AI advancements with VoucherVision and the Specimen Label Transcription Project (SLTP)

Natural history collections are critical reservoirs of biodiversity information but collections staff are constantly grappling with substantial backlogs and limited resources. The task of transcribing specimen label text into searchable databases requires a significant amount of time, manual labor, and funding. To address this challenge, we introduce VoucherVision, a tool harnessing the capabilities of several Large Language Models (LLMs; Naveed et al. 2023) to augment specimen label transcription. The VoucherVision tool automates laborious components of the transcription process, leveraging an Optical Character Recognition (OCR) system and LLMs to convert unstructured label text into appropriate data formats compatible with database ingestion. VoucherVision uses a combination of structured output parsers and recursive re-prompting strategies to ensure consistency and quality of the LLM-formatted text, significantly reducing errors. Integration of VoucherVision with the University of Michigan Herbarium’s transcription workflow resulted in a significant reduction in per-image transcription time, suggesting significant potential advantages for collections workflows. VoucherVision offers promising strides towards efficient digitization, with curatorial staff playing critical roles in data quality assurance and process oversight. Emphasizing the importance of knowledge sharing, the University of Michigan Herbarium is backing the Specimen Label Transcription Project (SLTP), which will provide open access to benchmarking datasets, fine-tuned models, and validation tools to rank the performance of different methodologies, LLMs, and prompting strategies. In the rapidly evolving landscape of Artificial Intelligence (AI) development, we recognize the profound potential of diverse contributions and innovative methodologies to redefine and advance the transformation of curatorial practices, catalyzing an era of accelerated digitization in natural history collections. An early, public version of VoucherVision is available to try here: https://vouchervision.azurewebsites.net/

Handwriting-Based Text Line Segmentation from Malayalam Documents

Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical projection values, the size of the handwritten characters, the height of the text lines and the curved nature of the Malayalam alphabet. The proposed technique is able to overcome incorrect segmentation due to the presence of characters written with spaces above or below other characters and the overlapping of lines because of ascenders and descenders. The performance of the proposed method for text line extraction is quantitatively evaluated using the MatchScore value metric and is found to be 85.507%. The recognition accuracy, detection rate and F-measure of the proposed method are found to be 99.39%, 85.5% and 91.92%, respectively. It is experimentally verified that the proposed method outperforms some of the existing language-independent text line extraction algorithms.

HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters

&lt;p&gt;Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.&lt;/p&gt;

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

Handwritten Digits and Optical Characters Recognition

The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and transform that text to ASCII text. OCR is beneficial and essential in many applications, such as information retrieval systems and digital libraries.