Character Recognition Research Articles

Enhanced scene text recognition using deep learning based hybrid attention recognition network

<span lang="EN-US">The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this research, is intended to greatly improve efficiency and accuracy of text recognition in complicated scene situations. HARN makes use of cutting-edge elements including alignment-free sequence-to-sequence (AFS) module, creative attention mechanisms, and hybrid architecture that blends attention models with convolutional neural networks (CNNs). Thanks to its novel attention processes, HARN is capable of comprehending wide range of scene text components by capturing both local and global context information. Through faster network convergence, shorter training times, and better utilization of computing resources, the suggested technique raises bar for state-of-the-art. HARN’s versatility makes it a good choice for range of scene text recognition applications, including multilingual text analysis and data extraction. Extensive tests are conducted to assess the effectiveness of HARN approach and demonstrate it is ability to greatly influence real-world applications where accurate and efficient text recognition is essential.</span>

Efficient deep learning models for Telugu handwritten text recognition

<p>Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.</p>

Detection and identification of un-uniformed shape text from blurred video frames

<p><span lang="EN-GB">The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask</span><span lang="EN-US"> r</span><span lang="EN-GB">egion convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology.</span></p>

Deep learning-based attention models for sarcasm detection in text

Deep learning-based attention models for sarcasm detection in text

Deep learning utilization in Sundanese script recognition for cultural preservation

This study addresses the challenge of preserving the Sundanese script, a traditional writing system of the Sundanese community in Indonesia, which is at risk of being forgotten due to technological advancements. To tackle this problem, we propose a deep learning approach using the YOLOv8 model for the automatic recognition of Sundanese characters. Our methodology includes creating a comprehensive dataset, applying augmentation techniques, and annotating the characters. The trained model achieved a precision of 95% after 150 epochs, demonstrating its effectiveness in recognizing Sundanese characters. While some variability in accuracy was observed for certain characters and real-time applications, the results indicate the feasibility and promise of using deep learning for Sundanese script recognition. This research highlights the potential of technological solutions to digitize and preserve the Sundanese script, ensuring its continued legacy and accessibility for future generations. Thus, we contribute to cultural preservation by providing a method to safeguard the Sundanese script against obsolescence.

Sarcasm detection on social data: heuristic search and deep learning

<span lang="EN-US">Due to the significant surge in online activity, sarcasm detection (SD) has attracted major attention in social media networks. Sarcasm is a lexical item of negative sentiments or dislikes by utilizing exaggerated language constructs. SD has created a natural language processing (NLP) procedure focused on the intricate and unclear aspects of sarcasm, primarily used in sentiment analysis (SA), human-computer interaction, and various NLP applications. Concurrently, advancements in machine learning (ML) approaches facilitate the creation of effective SD systems. This manuscript presents the future search algorithm with deep learning assisted sarcasm detection and classification on social networking data (FSADL-SDCSND) approach. The major intent of the FSADL-SDCSND approach is in the effective and automated recognition of sarcastic text. In the presented FSADL-SDCSND technique, several data pre-processing stages are achieved to transform the data into a compatible format. Besides, the FSADL-SDCSND approach applies a bidirectional serial-parallel long short-term memory (BS-PLSTM) approach for SD and classification. The hyperparameter tuning process is accomplished by employing the future search algorithm for improving the recognition of the BS-PLSTM model. For superior output of the FSADL-SDCSND model, a sequence of simulations can be applied. The investigational outputs highlighted the improved solutions of the FSADL-SDCSND model with other approaches under diverse performance measures.</span>

ProtoUDA: Prototype-Based Unsupervised Adaptation for Cross-Domain Text Recognition

ProtoUDA: Prototype-Based Unsupervised Adaptation for Cross-Domain Text Recognition

Fog and rain augmentation for license plate recognition in tropical country environment

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.

Emotion detection in text: advances in sentiment analysis

Emotion detection in text: advances in sentiment analysis

The Effect of a Sibling Shared Reading Intervention on the Reading Development of Early School-Aged Children in Rural China

ABSTRACT Research Findings: The home literacy environment provides important support for the reading development of preschool and early school-age children; however, existing research has mainly focused on the role of parents. The current study examines the effect of a 10-week, sibling-shared reading intervention on the reading development of lower-grade primary school children in rural China. Sixty children in grades 1–2 from multi-child families were recruited from a rural primary school and randomly assigned to the intervention and control groups (30 in each group). Results showed that although the two groups of children were well matched on various background characteristics and reading measures at baseline, immediately after the intervention, children in the Sibling Shared Reading intervention group not only performed significantly better than the control group on intervention-based character recognition but also showed significantly higher performance on reading comprehension beyond the intervention materials than the control group. At the six-month follow-up test, the intervention group also performed significantly better than the control group in reading comprehension. The findings of the present study extend the home literacy model and suggest the role of siblings in children’ reading development. Practice or Policy: We recommend that, in addition to parents, reading enrichment activities among siblings are an important and effective strategy for promoting children’s reading development in multi-child families.

A Metric-Based Detection System for Large Language Model Texts

More efforts are being put into improving Large Language Models’ (LLM) capabilities than into dealing with their implications. Current LLMs are able to generate high quality texts seemingly indistinguishable from those written by human experts. While offering great potentials, such breakthroughs also pose new challenges for safe and ethical uses of LLMs in education, science, and a multitude of other areas. To add up, the majority of current approaches in LLM text detection are either computationally expensive or need access to the LLMs’ internal computations, both of which hinder their public accessibility. With such motivation, this paper presents a novel metric learning paradigm for detection of LLM-generated texts that is able to balance among computational costs, accessibility, and performances. Specifically, the detection is based on learning a similarity function between a given text and an equivalent example generated by LLMs that outputs high values for LLM-LLM text pairs and low values for LLM-human text pairs. In terms of architecture, the detection framework includes a pretrained language model for the text embedding task and a newly designed deep metric model. The metric component can be trained on triplets or pairs of same-context instances to signify the distances between human texts and LLM ones while reducing that among LLM texts. Next, we develop five datasets totalling over 95,000 contexts and triplets of responses in which one from human and two from GPT-3.5 TURBO or GPT-4 TURBO for benchmarking. Experiment studies show that our best architectures maintain F1 scores in between 0.87 to 0.95 across the tested corpora in multiple experiment settings. The metric framework also demands significantly less time in training and inference compared to the RoBERTa, LLaMA 3, Mistral v0.3, and Ghostbuster, while keeping 90% to 150% performances of the best benchmark.

Comparative Analysis of Object Detection Models for Sheet Music Recognition: A Focus on YOLO and OMR Technologies

Abstract. As Artificial Intelligence (AI) technologies are developing rapidly and are widely used in various domains, it is efficient and convenient for composers to make music using AI to convert sheet music to audio. This research aims to compare the performance of different models in identifying individual notes within sheet music. Compared to traditional technologies like Optical Music Recognition (OMR), deep learning models have a significant advantage in processing blurry images with high efficiency. In the research process, three different models are used in searching for musical notes: OMR, You Only Look Once (YOLO)v5, and YOLOv8. The evaluation index consists of recognition accuracy, mean Average Precision (mAP), inference speed, and parameter quantity. After the experiment, it is found that the YOLO model performs best with high accuracy and fast speed. Based on the above analyses, the thesis finds that the YOLO model can be an efficient tool in composing music, with further research.

Retraction Note: Optical handwritten character recognition for Tamil language using CNN-VGG-16 model with RF classifier

Retraction Note: Optical handwritten character recognition for Tamil language using CNN-VGG-16 model with RF classifier

Natural Language Processing for Electronic Health Record Optimization in Android Applications

This paper aims to demonstrate the development of an Android application designed to improve healthcare data management using Natural Language Processing (NLP) technologies. It develops an Android application that enables healthcare professionals to access patient records anytime, enhancing decision-making. The system uses Optical Character Recognition (OCR) to extract patient data from documents, simplifying data entry for diagnoses and treatments. It also incorporates Quick Response (QR) code generation for accurate data retrieval. By leveraging NLP, the mobile application will enhance efficiency, accessibility, and accuracy in healthcare, particularly benefiting developing countries where digital health records are uncommon..

Vision Aid: Developing An Assistive Mobile Application for Visually Impaired Indivuals

Vision Aid is a cutting edge smartphone software that combines several assistive technologies into a unified platform to help visually impaired individuals. Integrating Computer Vision algorithms this project makes use of Bing Maps API for Step by Step navigation, Tesseract OCR for text recognition, YOLO (You Only Look Once) for real time object detection and CLIP (Contrastive Language-Image Pre training) for scene description. It offers voice commands, audio feedback for a more improved day to day life hence improving self-confidence while empowering independence to the users. This journal provides a thorough description of Vision Aid the development process, methodology, evaluation results and shows the potential for future improvements with a goal to advance mobile accessibility for visually impaired individuals

A Survey on Multimodal Large Language Models

Abstract Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and Optical Character Recognition (OCR)-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of MLLMs. First of all, we present the basic formulation of MLLM and delineate its related concepts, including architecture, training strategy and data, as well as evaluation. Then, we introduce research topics about how MLLMs can be extended to support more granularity, modalities, languages, and scenarios. We continue with multimodal hallucination and extended techniques, including Multimodal ICL (M-ICL), Multimodal CoT (M-CoT), and LLM-Aided Visual Reasoning (LAVR). To conclude the paper, we discuss existing challenges and point out promising research directions.

A Multi-Level Embedding Framework for Decoding Sarcasm Using Context, Emotion, and Sentiment Feature

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research.

Reverse Sign Language Recognition System Using Machine Learning

Deaf and hard-of-hearing people communicate with each other and their society by using sign language. As this is a very natural method for pupils to stay in touch with computers, many academics are working on it to make it less complex and more convenient for use. So the main objective of gesture recognition research is to make systems which can interact and communicate while understanding human gestures and use them. In simple words to communicate information. Fast and extremely accurate hand detection and realtime hand gestures. Identification should be possible with vision-based hand gesture circumstances and interfaces. Learning and knowing sign movements and gestures is the kick start in making words and sentences for computer assisted sign language interpretation. Both dynamic and static sign actions are open and available. Both ways of gesture recognition are crucial to human culture, even if static gesture recognition is much easier than dynamic gesture recognition. When a human enters the value of alphabets and numerical value as input, the system immediately displays or outputs the appropriate recognised character shows the gesture on the monitor screen. In the following, research projects that have led to a proper system that uses convolutional neural networks to identify handwriting on the basis of the depth pictures and Hand Languages (Brain lipi) the collects. Keywords- Convolutional neural network,Text recognition ,Convert to image,StackGAN,simultaneous Tracking text and converting to image, gesture recognition,display output as sign gesture,training machine to(A-z to 1-0).

Research on Closed-Loop Control of Screen-Based Guidance Operations in High-Speed Railway Passenger Stations Based on Visual Detection Model

Due to adjustments to the operation plan of guided trains at high-speed railway stations, a large amount of information is inevitably displayed, sometimes with delays, omissions, and misalignments. The effective management of guidance information can provide important support for the personnel flow operation of high-speed railway stations. Aiming to meet the requirements of high real-time and high accuracy of guided job control, a closed-loop control method based on a guided job is proposed, which provides enhanced text detection and recognition in a target area. Firstly, using the introduction of the triplet attention mechanism in YOLOv5 and the addition of fusion modules, the feature pyramid network is used to enhance the effective feature and feature interactions between the modules to improve the detection speed of the display. Then, the text on the guide screen is recognized and extracted in combination with the PaddleOCR model, and then, the results are proofread against the original plan to adjust the screen information. Finally, the effectiveness and feasibility of the method are verified by experimental data, with the accuracy of the improved model reaching 90.6% and the speed reaching 1 ms, which meets the requirement of real-time closed-loop control of Screen-Based Guidance Operations.

Application of Binary Image Quality Assessment Methods to Predict the Quality of Optical Character Recognition Results

One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results’ quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results.