Utilization of Optical Character Recognition Technology in Reading Identity Cards

OCR2SEQ represents an innovative advancement in Optical Character Recognition (OCR) technology, leveraging a multi-modal generative augmentation strategy to overcome traditional limitations in OCR systems. This paper introduces OCR2SEQ’s unique approach, tailored to enhance data quality for sequence-to-sequence models, especially in scenarios characterized by sparse character sets and specialized vocabularies. At the heart of OCR2SEQ lies a set of novel augmentation techniques designed to simulate realistic text extraction errors. These techniques are adept at generating diverse and challenging data scenarios, thereby substantially improving the training efficacy and accuracy of text-to-text transformers. The application of OCR2SEQ has shown notable improvements in data processing accuracy, particularly in sectors heavily dependent on OCR technologies such as healthcare and library sciences. This paper demonstrates the capability of OCR2SEQ to transform OCR systems by enriching them with augmented, domain-specific data, paving the way for more sophisticated and reliable machine learning interpretations. This advancement in OCR technology, as presented in the study, not only enhances the accuracy and reliability of data processing but also sets a new benchmark in the integration of augmented data for refining OCR capabilities.

Optical character recognition (OCR) technologies have known an effervescent development in last decade. Development was strongly influenced by the development of hardware, advance in image processing, and classification algorithms. There are multiple OCR technologies available, each of them based on different approaches, e.g., geometric processing or cognitive learning based on neural networks. One critical parameter for each of those approaches is execution time. In our opinion a very important percent of text used to be “OCRed” is coming from preprinted documents and forms, which are bounded by various regulations in layout and/or contained information. Based on this observation we argue that most of the characters that must be recognized have a similar layout, thus improvement of the processing performance can be obtained by creating classes of similar characters (blobs) based on geometric similarities, and performing OCR only on the representative blob from each class. In this paper we will present the architecture of an OCR technology based on a multilayer neural network. Performance improvement has been obtained using a blob classifier that groups characters in classes, and then perform OCR only on the representative blob from each class.

The advances in Optical Character Recognition (OCR) technology over the past decade have enabled the development of many automatic document-processing systems capable of 99% correct recognition on printed text. However, similar advances in Cursive Script Recognition (CSR) technology have not been forthcoming due, principally, to the vast variability of human handwriting. This paper investigates a method by which the more reliable OCR technology can be used to improve the CSR performance in a form processing application. A novel method is proposed to link handwriting data to contextual cue words that have been automatically obtained from an OCR process. This information is then used to select appropriate 'focused' lexicons to achieve better CSR results. The method was tested on 30 forms that were filled by 10 different writers. The experimental results together with a comparison to the base line recognition performance are presented.

In recent years, with the increasing usage of digital media and advancements in deep learning architectures, most of the paper-based documents have been revolutionized into digital versions. These advancements have helped the state-of-the-art Optical Character Recognition (OCR) and digital mailroom technologies become progressively efficient. Commercially, there already exists end to end systems which use OCR and digital mailroom technologies for extracting relevant information from financial documents such as invoices. However, there is plenty of room for improvement in terms of automating and correcting post information extracted errors. This paper describes the user-involved, self-correction concept based on the sequence to sequence Neural Machine Translation (NMT) as applied to rectify the incorrectness in the results of the information extraction. Even though many efficient Post-OCR error rectification methods have been introduced in the recent past to improve the quality of digitized documents, they are still imperfect and demand improvement in the area of context-based error correction specifically for the documents involving sensitive information. This paper further illustrates the capability of sequence learning with the help of feedback provided during each cycle of training, yields relatively better results and have outsmarted the state-of-the-art OCR error correction methods.

In 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.

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures.

Optical character recognition (OCR) technology is widely used to convert scanned documents to text. However, historical books still remain a challenge for state-of-the-art OCR engines. This work proposes a new approach to the OCR of large bodies of text by creating an adaptive mechanism that adjusts itself to each text being processed. This approach provides significant improvements to the OCR results achieved. Our approach uses a modified hierarchical optical flow with a second-order regularization term to compare each new character with the set of super-symbols (character templates) by using its distance maps. The classification process is based on a hybrid approach combining measures of geometrical differences (spatial domain) and distortion gradients (feature domain).

The healthcare industry continues to depend extensively on paper-based prescriptions, frequently transmitted through fax systems, to ensure compliance with stringent security and regulatory standards. This reliance introduces significant challenges to manual processing, which is often labor-intensive, error-prone, and inefficient, thereby increasing the risk of medication errors and compromising patient safety. Optical Character Recognition (OCR) technology offers a promising solution by automating the conversion of handwritten and printed prescription images into machine-readable text, thereby enhancing the accuracy and efficiency of healthcare workflows. This paper presents a detailed review of OCR methodologies, focusing on feature extraction, classification, and segmentation techniques that are critical for achieving high recognition accuracy in complex handwritten texts, such as medical prescriptions. By leveraging advanced pattern recognition approaches, including neural networks and hybrid models, OCR systems can significantly reduce manual effort and errors in prescription processing. This study emphasizes the importance of selecting relevant feature extraction methods and robust classification algorithms to handle the variability and noise inherent in re-faxed prescriptions, thereby improving the reliability of automated prescription recognition systems. This study aims to guide and update researchers and practitioners in developing efficient OCR solutions tailored to the healthcare domain, ultimately contributing to safer and more efficient medication administration processes.

Historical newspapers are invaluable resources that capture the cultural, social, economic, and political dimensions of their time, providing unique insights into past events and daily life. However, the potential of these archives remains underutilised due to the limitations of traditional research methods, which rely on laborious manual examination of individual pages or articles. Digital archives have begun to address this issue by enhancing accessibility, but the lack of effective tools for parsing and analysing the contents still restricts their utility for large-scale historical research. Optical Character Recognition (OCR) technology plays a foundational role in converting scanned images into searchable text. However, OCR alone cannot adequately handle the complexities of historical newspapers, which often feature degraded image quality, inconsistent typography, and intricate layouts. In parallel, the effective segmentation and ordering of newspaper contents is essential for generating well-structured and logically ordered data from specific newspaper sections. This paper focuses on the development of a pipeline that addresses these challenges, using the daily newspaper Le Sémaphore de Marseille as a case study (35,703 issues covering the period 1827 to 1944). The proposed system integrates layout analysis, OCR, and Generative AI-driven information extraction techniques to extract specific data elements from scanned images, namely ship arrivals data, and to store them in a machine-readable format for further analysis. This well-structured output enables historians to analyse historical trade patterns, economic trends, and regional interactions more efficiently and at a much larger scale. An evaluation of the key pipeline components demonstrates the effectiveness of the system, achieving an F1-score of 96% in paragraph segmentation and information extraction, while also highlighting the need for further improvement in the critical OCR component.

This study aimed to develop a technology to recognize and warehouse text data from a table containing technical documents relating to construction and plant projects. To this end, a table optical character recognition (OCR) technology was proposed to tag text data by recognizing the structure of the table and the context of the content in the table. For analysis, the table format was first classified into two patterns: T1 and T2. T1 refers to a table with only one step of the header, and T2 refers to a table with two phases of the header. The table OCR model extracts text in cell units of the table using the OpenCV engine after extracting data from the headers. A training model improves text recognition rate through a long short-term memory (LSTM)-based Tesseract OCR engine. Extracted data from the table were stored in the DB and output in CSV format. The confusion matrix was applied to verify the recognition accuracy of the extracted data, and as a result of the verification, the F-measure value of T1 was 96%, and T2 was 87%. Therefore, from the outcome of this study, it is expected that the automated management of tasks that hitherto relied solely on the engineer’s experience will subsequently contribute to reducing the workload and improving the productivity of the engineer in charge.KeywordsAutomatic conversion of table contentsTable OCROpenCVTesseract OCR

This set consists 8630 volumes, published between 1870-1879. The dataset comprises text from the collection of digitised books created using Optical Character Recognition (OCR) technology. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The dataset is in Analysed Layout and Text Object (ALTO) Extensible Markup Language (XML) format.

Accessible communication is essential for the inclusion of deaf people in scientific and cultural society. The language barrier that deaf people face when trying to access written Spanish can limit their self-efficacy and their ability to participate fully in society. However, Optical Character Recognition (OCR) technology and Artificial Intelligence offer an innovative solution: the application of a Spanish signifier translator to Colombian Sign Language (CSL). This tool allows deaf people to understand their environment in Spanish in an autonomous way, which promotes inclusion and active participation in the scientific and cultural society. The application of OCR technology in the detection, reading, and conversion of Colombian Sign Language is essential to provide better living conditions for people with hearing disabilities and to bring together two cultures that have traditionally been distanced by the language barrier. This article describes the stages of development of the mobile application, the technology used, and the role of the sign language translator in promoting accessible communication and the participation of deaf people in a scientific and cultural society.

This paper presents a groundbreaking development in assistive innovation through the improvement of visionary smart glasses custom-made for people with visual impedances. These smart glasses are equipped with a Raspberry Pi Camera module designed to capture images of printed materials such as daily papers, books, and other objects of interest. The captured visual information is then handled through advanced optical character recognition (OCR) technology, changing over it into text. Hence, the text information is consistently transformed into audible content using text-to-speech (TTS), which the user can tune in to through an associated headset.The main objective of these visionary smart glasses is to enable the visually challenged by giving real-time access to printed materials and improving their ability to independently engage with composed data. Furthermore, these smart glasses incorporate a second module outlined for cash reading, permitting users to precisely distinguish and manage distinctive categories of currency. This dual-functionality aims to extend availability and independence, subsequently cultivating more prominent inclusivity and quality of life for individuals with visual impairments.The advancement of these visionary smart glasses represents a critical step towards bridging the data gap that frequently hinders the visually challenged community. By consistently converting printed materials into audio and empowering quick and reliable cash identification, these inventive smart glasses are poised to revolutionise the way people with visual disabilities interact with their environment, eventually enhancing their freedom and overall well-being. Keywords— Envisioning, Revolutionizing, Raspberry Pi, Camera Module, OCR, TTS, Currency.

This project aims to develop a system that can convert image text into speech using Raspberry Pi. This system will use optical character recognition (OCR) technology to extract text from images, which will then be converted into speech using text-to-speech software. (TTS). (TTS). This will provide a valuable tool for individuals with visual impairments or those who have difficulty reading text in images. This research explores a Raspberry Pi-based device that can translate English into 53 dialects, using a camera module, OCR motor, Google Speech API, and Microsoft Translator. This feature is accessible to visually impaired individuals and those who do not speak English. Raspberry Pi, with the Tesseract OCR engine, Google Voice API, Microsoft Translator, and camera board, enables real-time text translation on images, making it more accessible and inclusive for users with visual impairments or language barriers.

Phishing is currently defined as a criminal mechanism employing both social engineering and technical subterfuge to gather any useful information such as user personal data or financial account credentials. Many users are sensible about this kind of attack from suspicious URL addresses or obvious warning information from browsers, but phishing still accounts for a larger proportion of all of malicious attacks. Moreover, these warning features will be eliminated if the victim is under a DNS hijacking attack. There is much research about the prevention and evaluation of phishing, in both PC platforms and mobile platforms, but there are still technical challenges to reducing the risk from phishing, especially in mobile platforms. We presented a novel method to prevent phishing attacks by using an Optical Character Recognition (OCR) technology in a previous paper. This method not only overcomes the limitation of current preventions, but also provides a high detection accuracy rate. However, whether this method can be implemented ideally in mobile devices needed to be further examined, especially in relation to the challenges of limited resources (power and bandwidth). In this paper, we apply the OCR method in a mobile platform and provide a prototype implementation scheme to determine applicability. Experiments are performed to test the technique under DNS hijacking attacks.