Character Recognition Techniques Research Articles

Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction

ObjectiveTo assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously. Patients and MethodsIn this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 2022 to April 2024. ResultsThis streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably. ConclusionBy implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.

Methodology for eliminating plain regions from captured images

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

Digitization Of Tulu Handwritten Scripts- A Literature Survey

Applications for handwritten script digitization include automated form processing, archival preservation, document digitization, handwriting recognition software, and historical document research. The performance of the recognition algorithms, the complex nature of the script, and the handwriting quality all affect how accurate the digitization is. Significant research on handwritten text recognition in scripts like Latin, Chinese, Arabic, and Japanese is reported. Still in its infancy, the work described on handwritten Indic scripts necessitates extensive research in this area. This thorough analysis offers an open view of different feature extraction and classification methods for the offline and online identification of handwritten Indic scripts.The primary objective of this paper is to explore the accuracy and efficiency of recognition systems by analyzing several state-of-the-art studies and comparing and evaluating various techniques for Tulu script handwritten character recognition. This paper aims to describe various advancements reported over the last few decades in the field of handwritten scripts recognition.

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

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

INAPPROPRIATE CONTENT DETECTION IN IMAGE

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

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

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

Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.

Handwritten character recognition is one of the classical problems in the field of image classification. Supervised learning techniques using deep learning models are highly effective in their application to handwritten character recognition. However, they require a large dataset of labeled samples to achieve good accuracies. Recent supervised learning techniques for Kannada handwritten character recognition have state of the art accuracy and perform well over a large range of input variations. In this work, a framework is proposed for the Kannada language that incorporates techniques from semi-supervised learning. The framework uses features extracted from a convolutional neural network backbone and uses regularization to improve the trained features and label propagation to classify previously unseen characters. The episodic learning framework is used to validate the framework. Twenty-four classes are used for pre-training, 12 classes are used for testing and 11 classes are used for validation. Fine-tuning is tested using one example per unseen class and five examples per unseen class. Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.

INVOICE DATA EXTRACTION USING OCR TECHNIQUE

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

Candidate Authentication using OCR Techniques

This paper explores the application of Optical Character Recognition (OCR) techniques for authenticating candidates based on identity documents. As advancements in technology continue to redefine various industries, the integration of OCR into candidate authentication processes offers a streamlined and efficient solution. The aim is to design a system which gets the image of the identity proof and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). The authentication process encompasses document validation and implementation of security measures to safeguard against fraud.By combining YOLO object detection, Optical Character Recognition (OCR) and fuzzy matching technique ,this approach aims to enhance the accuracy and reliability of candidate authentication. This underscores the importance of a comprehensive authentication framework, incorporating both technological and procedural elements, to establish a robust and secure candidate authentication process in diverse contexts. Keywords: ID Verification,Optical Character Recognition (OCR), Text Extraction, YOLO,Object Detection,Image Segmentation, EasyOCR , Fuzzy Matching.

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

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

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

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

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

Various opinions were made by the researchers to develop an automatic network for Optical Character Recognition (OCR). Still, character recognition in handwritten scripts is an unsolved task. In this paper, two efficient techniques are developed an effective character recognition technique for the handwritten Kannada scripts. The Kannada Character Recognition (KCR) techniques faced several challenges due to the different writing styles of people and the absence of fixed spacing among alphabets, words and lines. Another complication in the KCR model is the absence of large datasets to train the network, and it isn’t easy to write the Kannada script by combining the Kannada alphabets. Therefore, a new handwritten KCR approach is developed to identify the characters from the ancient Kannada scripts. The required Kannada script images are gathered from various online databases. The garnered images are preprocessed and segmented using morphological operation and thresholding. The relevant features from the images are achieved by the geometric feature extraction method. Finally, the characters are recognized by utilizing the Long Short Term Memory (LSTM) network, and the experimental results will be analyzed over the traditional optimization strategies and baseline works to evaluate the efficiency of the proposed network.

Smart Eye for Visually Impaired People

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

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

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

Face and number plate recognition for car anti-theft

Smart parking systems along with that is continuous development of new technologies, are widely applied to improve our lives. It can also add new technologies with advanced functions making it a multi-functional management system. Thanks to the anti-theft technologies that are installed, modern cars are significantly more difficult to steal than they once were. However, electrical systems can still experience issues, though and malfunction at some point. This paper suggests using video image recognition technology, at car parks and parking lots as an anti-theft solution, alerting the presence of the non-owner of vehicle in the driver’s seat. The system automatically predicts whether the driver is valid with the registration number plate. The image of the car is captured by the camera at the entrance gates of the parking lot. The proposed algorithm includes face recognition in images, building a deep learning convolutional network that classifies faces (subscriber’s images); using Cascade trainer to train number plate object recognition, vehicle number recognition through character recognition technique. The system can recognize reality through a personal computer connected to the camera at the scene or through photos and video files. Result, the model can face recognition and match to license plate in at a moment.

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

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

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

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

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

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

Number Plate Detection in an Image

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