Optical Character Recognition Technology Research Articles

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

Envisioning Sight Beyond Sight: Revolutionizing Visionary Assistive Smart Glasses for Visually Challenged

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.

Computer Vision-Based Mobile Application for Visually Impaired

In our society, there's a growing need for awareness and support, especially for individuals facing visual impairments. This endeavor revolves around creating a specialized mobile application precisely catering to their unique needs. Harnessing the capabilities of computer vision and deep learning, the app functions as a versatile aid, offering real-time support and enhancing accessibility for its users. Through the Smartphone’s camera, the application can identify and describe various objects, allowing users to gain a better understanding of their immediate environment. Furthermore, the integration of optical character recognition (OCR) technology enables the app to convert printed text into audio, providing users with access to a wide range of printed materials. The navigation assistance feature utilizes advanced algorithms to detect obstacles and provide users with alerts, ensuring safe and efficient navigation.

Transformative Advances in PDF Information Management: Leveraging Transformer Models for Contextual Query-Answering

In the information-driven world of today, efficiently extracting and comprehending data from a variety of document formats, especially scanned images and PDFs, is a major challenge. Standard methods often fall short of extracting contextually aware insights from these documents. In order to provide contextual query-answering, this paper introduces a transformative approach to PDF information management by combining OCR technology with a transformer model, namely GPT-2. The system uses OCR to turn scanned documents and images into text, and the GPT-2 is used to contextually translate and understand the text. By using this innovative technique, query-based information retrieval becomes much more accurate and efficient, giving users precise, context-aware responses to their questions.The proposed method offers improved data accessibility and optimised information management, setting a new benchmark in document analysis across different sectors. This work contributes to the advancement of document interaction and understanding by combining OCR technology with sophisticated transformer models, paving the way for future developments in natural processing and document processing. Key Words: Machine Learning, Optical Character Recognition (OCR), Generative Pre-trained Transformer 2 (GPT-2), Natural Language Processing (NLP), FastAPI.

PERANCANGAN SISTEM WAREHOUSE BERBASIS TEKNOLOGI OCR UNTUK MENINGKATKAN EFEKTIVITAS DAN EFISIENSI

PERANCANGAN SISTEM WAREHOUSE BERBASIS TEKNOLOGI OCR UNTUK MENINGKATKAN EFEKTIVITAS DAN EFISIENSI

Characterizing Natural Hydrogen Occurrences in the Paris Basin From Historical Drilling Records

AbstractThis study investigates natural hydrogen (H2) occurrences in the Paris Basin, using Optical Character Recognition (OCR) technology to analyze an extensive, yet underexploited, database that contains historic drilling records. The potential of natural hydrogen has been largely unexplored in conventional oil and gas wells. Utilizing the in‐house CVAGeoDB database based on public well data, which includes well logs, mudlogs, and End Drilling Reports (EDRs) in PDF image format, we applied the Tesseract‐OCR Engine to convert these documents into searchable formats for efficient data analysis. Our analysis revealed several H2‐bearing wells across French sedimentary basins. The hydrogen occurrences in the Aquitaine Basin may be explained by the geological context and in particular the presence of a mantle body at shallow depth. On the contrary, the detection of H2 in the Paris Basin cannot be explained in a straightforward manner as the presence of ultramafic or U‐rich rocks is poorly documented so far. In the Paris Basin, H2 has been detected in four main formations: the Lusitanian, the Dogger, and Triassic aquifers as well as in the basement. The highest hydrogen concentration (52 vol%) was measured in the Dogger aquifer. These wells are primarily located along the Bray Fault, indicating at least a structural influence on H2 distribution. Finaly, the presence of serpentinzed dunite from the Lizard complex associated with the bedrock may have played the role as a source for H2. This research demonstrates the effectiveness of OCR in reassessing historical drilling data for natural hydrogen exploration, highlighting the need for comprehensive exploration methodologies in this emerging field.

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.

IoT-driven accessibility: A refreshable OCR-Braille solution for visually impaired and deaf-blind users through WSN

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.

Enhancement of handwritten text recognition using AI-based hybrid approach

Handwritten text recognition (HTR) within computer vision and image processing stands as a prominent and challenging research domain, holding significant implications for diverse applications. Among these, it finds usefulness in reading bank checks, prescriptions, and deciphering characters on various forms. Optical character recognition (OCR) technology, specifically tailored for handwritten documents, plays a pivotal role in translating characters from a range of file formats, encompassing both word and image documents. Challenges in HTR encompass intricate layout designs, varied handwriting styles, limited datasets, and less accuracy achieved.Recent advancements in Deep Learning and Machine Learning algorithms, coupled with the vast repositories of unprocessed data, have propelled researchers to achieve remarkable progress in HTR. This paper aims to address the challenges in handwritten text recognition by proposing a hybrid approach. The primary objective is to enhance the accuracy of recognizing handwritten text from images. Through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a Connectionist Temporal Classification (CTC) decoder, the results indicate substantial improvement. The proposed hybrid model achieved an impressive 98.50% and 98.80% accuracy on the IAM and RIMES datasets, respectively. This underscores the potential and efficacy of the consecutive use of these advanced neural network architectures in enhancing handwritten text recognition accuracy.•The proposed method introduces a hybrid approach for handwritten text recognition, employing CNN and BiLSTM with CTC decoder.•Results showcase a remarkable accuracy improvement of 98.50% and 98.80% on IAM and RIMES datasets, emphasizing the potential of this model for enhanced accuracy in recognizing handwritten text from images.

Implementing AI-Driven Efficiency: Best Practices for Intelligent Order Processing in SAP

Abstract: In today's hyper-competitive business environment, streamlining order processing is crucial for maximizing efficiency, minimizing errors, and fostering customer satisfaction. Traditional methods, often manual and error-prone, struggle to keep pace with the demands of modern commerce. This paper delves into the transformative power of Artificial Intelligence (AI) in revolutionizing order processing within SAP. the leading Enterprise Resource Planning (ERP) system. The article explores the integration of Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies, shedding light on how this union reshapes traditional workflows. From streamlining data entry processes to empowering intelligent decision-making, the article delineates the best practices for organizations seeking to harness the power of AI in SAP order processing. The discussion encompasses key considerations before implementation, seamless integration strategies, and best practices in training AI models. Real-world case studies illustrate successful implementations, highlighting the tangible benefits achieved in terms of efficiency, accuracy, and overall workflow optimization.

Research on a Web System Data-Filling Method Based on Optical Character Recognition and Multi-Text Similarity

In the development of web systems, data uploading is a relatively important function. The traditional method of uploading data is to manually fill out forms, but when the data to be uploaded mostly exist in the form of form images, and the form content contains a lot of similar field information and irrelevant edge information, using traditional methods is not only time-consuming and labor-intensive, but also prone to errors. This requires a technology that can automatically fill in complex form images. OCR is an optical character recognition technology that can convert images into digitized text data using computer vision methods. However, using this technology alone cannot complete the tasks of extracting relevant data and filling corresponding fields. To address this issue, this article proposes a method that combines OCR technology and Levenshtein multi-text similarity. This method can effectively solve the problem of data filling after parsing complex form images, and the application results of this method in web systems show that the filling accuracy for complex form images can reach over 90%.

Advantages of Automated License Plate Recognition Technology

Automatic Number Plate Recognition (ANPR) is a technology that can be utilized by mall and movie theater parking management as well as toll booths on roads, expressways, etc. to expedite the toll collection process. Automated systems to retain vehicle information are becoming more and more important as the number of vehicles rises. It is imperative that communication be prioritized in order to effectively manage traffic and reduce crime. Number plate recognition is a dependable technique for automatic vehicle identification. With current algorithms, which are based on the concept of learning, developing adequate outcomes takes a long time and a lot of experience. An innovative use of machine learning is Automatic Number Plate Detection, which recognizes photos and transforms them into text. Using picture segmentation, the system locates, picks up, and extracts the vehicle's image and number plate. Afterwards, optical character recognition technology is used to identify characters in the retrieved image. This technology is used in military zones, apartment buildings, and traffic surveillance.

Digitizing Metadata of a University Fashion Collection’s Holdings Using OCR and Costume Core

To address the lack of standardized metadata in the historic dress and costume field and increase the searchability and discoverability of online fashion collections, a university collection adopted Costume Core, a metadata schema with controlled vocabulary for fashion artifacts, to develop the digital collection and to assess and improve the usability of the schema. This paper presents a case study delving into the process, benefits, and limitations of using Costume Core to catalog fashion artifacts. It includes a discussion on converting handwritten and typewritten metadata from physical catalog cards into a digital text format using Optical Character Recognition (OCR) technology. Costume Core provided a fundamental framework for digitizing and cataloging the university fashion collection while ensuring consistency in the terminology used to describe artifacts in the collection. Nevertheless, there is room for improvement for Costume Core, particularly by expanding the schema by adding controlled terms to increase the accuracy of terms chosen to describe fashion artifacts. Therefore, work is underway to increase its usability, thus promoting its adoption among other fashion collections.

An End-to-End Tag Recognition Architecture for Industrial Meter

Optical character recognition (OCR) technology is promoting the process of automation in daily information recording and inspection of industrial meters. However, bad installation scenes and long-time running cause industrial meters to age to varying degrees, which increases the difficulty of OCR. Besides, in practice, operators typically use handheld cameras to extract tag information from industrial meters. During the shooting process, extreme lighting, free shooting angles, and shooting distance also cause many difficulties for OCR. Considering such difficulties, an end-to-end recognition architecture is developed to obtain a better OCR performance. The proposed architecture can quickly extract structured information from normal or skewed text images. A novel yolov5_adaloss with a specific penalty factor is designed to alleviate the influence of illumination, installation scene, age, skew, and distance on the classification accuracy of Tags. For images with large skew angles, a mathematical method is proposed to calculate the text inclination angle for better OCR performance. The contribution of this work is twofold. Firstly, the architecture proposed is lightweight, which only needs a low computing cost and a short inference time. Secondly, as a practical application-oriented architecture, this work does not require much training, labeling, and fine-tuning, which is easy to generalize to other structured text recognition tasks. Experiments show that the method proposed in this paper can achieve excellent performance in meter tag recognition tasks on actual industrial images of meters.

OCR for devanagari script

The introduction of Optical Character Recognition (OCR) technology revolutionized text digitization, allowing physical documents to be converted into editable and searchable digital representations. This paper goes into the unique challenges and advancements in OCR designed exclusively for the Devanagari script. It helps to preserve cultural heritage by digitizing historic manuscripts and religious writings and making them more accessible. Furthermore, Devanagari OCR has practical uses in administrative activities, data entry, and educational content digitization.

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.

Digitization and Archiving of Company Invoices using Deep Learning and Text Recognition-Processing Techniques

Nowadays, it is crucial to transfer official documents such as invoices, dispatch notes, and receipts into digital environments and establish correct semantic relationships. However, understanding and processing these documents is a difficult process that requires significant time and effort. In recent years, the use of deep learning, image preprocessing, text detection, and optical character recognition (OCR) technologies have made this process easier. However, for text recognition and processing techniques to produce accurate results, documents must be clean and readable. Additionally, difficulties arising from time-consuming, tiring, error-prone, and cost-incurring human-powered digitalization processes must be reduced. The aim of this study is to digitize and archive scanned invoices and similar official documents using current artificial intelligence technologies, thereby enabling the most effective use of components such as time, cost, and human resources. The dataset used in the study includes 10,000 ".jpg" image files and 10,000 ".xml" data files. The model trained with the ResNet-50 architecture can detect text with accuracy rates of up to 97% on randomly selected images from the dataset. In an environment where a person can process an average of 2,112 documents per month, it is predicted that the trained artificial intelligence model can process 108,000 documents per month. With this developed method, businesses can quickly digitize and archive official documents such as invoices, dispatch notes, and receipts. Future studies propose the development of new methods that can produce better results using larger and more diverse datasets.

A Study of The OCR Development History and Directions of Development

Optical character recognition (OCR) is a well-established technology that enables the conversion of scanned images and documents into editable and searchable electronic text. This technology has numerous applications across a range of industries and has proven to be a crucial tool for digitizing books, documents, and records. One of the main benefits of OCR technology is its ability to automate data entry processes, saving time and reducing errors that can be introduced through manual data entry. Additionally, OCR technology is used to extract text from images, which can be used as input data for machine learning and artificial intelligence applications. To better understand the current state of OCR technology, this systematic literature review is conducted that collected various research articles on the topic. The results of this paper provide insights into the strengths and limitations of OCR technology and also offer directions for future research in the field. In terms of language support, the paper found that OCR technology is capable of supporting a wide range of languages, including English, Spanish, Chinese, and many others. However, the accuracy of OCR technology can vary greatly based on the language, with some languages being more challenging to recognize than others. With continued advancements in technology and the increasing need for digitization, OCR technology will continue to play a crucial role in the development of many industries.

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.

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.