Character Recognition Research Articles

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.

Text kernel expansion for real-time scene text detection

Text kernel expansion for real-time scene text detection

Ensemble automated approaches for producing high‐quality herbarium digital records

AbstractPremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates.ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.

Text Detection on Industrial Barrel Label with Convolutional Attention and Dual‐Branch Feature Network

Industrial barrel labels generally have low visual contrast, uneven lighting, and cluttered background, making it challenging to accurately locate text regions. This paper proposes a text detection network to solve the inaccurate localization problem based on DBNet. First, a convolutional attention mechanism is applied to the feature extraction network to get more valuable text feature maps. Then, a dual‐branch convolutional feature module is proposed in the feature pyramid to enrich contextual information. Besides, during the probability map generation stage, using a feature remodeling enhancement module to further distinguish text and text boundaries. This paper designs comparative experiments on ILTD, ICDAR2015 and MSRA‐TD500 datasets, achieve F‐measure of 92.3%, 86.0% and 84.1%, which are 2.2%, 2.3%, and 1.9% higher than DBNet, respectively. They demonstrate that our proposed method exhibits competitive performance and strong robustness. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Consumer Usability Test of Mobile Food Safety Inquiry Platform Based on Image Recognition

Recently, as the types of imported food and the design of their packaging become more complex and diverse, digital recognition technologies such as barcodes, QR (quick response) codes, and OCR (optical character recognition) are attracting attention in order to quickly and easily check safety information (e.g., food ingredient information and recalls). However, consumers are still exposed to inaccurate and inconvenient situations because legacy technologies require dedicated terminals or include information other than safety information. In this paper, we propose a deep learning-based packaging recognition system which can easily and accurately determine food safety information with a single image captured through a smartphone camera. The detection algorithm learned a total of 100 kinds of product images and optimized YOLOv7 to secure an accuracy of over 95%. In addition, a new SUS (system usability scale)-based questionnaire was designed and conducted on 71 consumers to evaluate the usability of the system from the individual consumer’s perspective. The questionnaire consisted of three categories, namely convenience, accuracy, and usefulness, and each received a score of at least 77, which confirms that the proposed system has excellent overall usability. Moreover, in terms of task completion rate and task completion time, the proposed system is superior when it compared to existing QR code- or Internet-based recognition systems. These results demonstrate that the proposed system provides consumers with more convenient and accurate information while also confirming the sustainability of smart food consumption.

Similarity Distractors Increase the Burden of Chinese Character Selection and Updating in Working Memory.

Attentional mechanisms are the primary processes for performing working memory (WM) tasks and can prevent distractors from interfering with the content representations stored in WM. However, our understanding of the mechanisms by which attention affects WM remains limited. As such, we analyzed ERPs of the character n-back task to investigate Chinese character selection, updating, and maintenance in WM. In Experiment 1, we collected electroencephalography data from 27 participants aged 18-25 years to explore the influence of false-character interference and symbol interference on a neural activity in the character n-back task. The results suggest that RT was longer in the false-character interference condition. The N2pc and P300 amplitudes were smaller; however, the slow wave amplitude did not differ significantly. In Experiment 2, we used a single-symbol interference and a multiple-symbol interference to establish whether the number of interferences affected the neural activity in the character n-back task. Thirty participants (aged 19-25 years) took part in the experiment. The findings imply that a longer RT and a larger N2pc amplitude occurred in the multiple-symbol interference condition, but not in the P300 and slow wave conditions. Our findings indicate that distractors that are similar to characters may produce greater interference in character recognition and affect the subsequent updating, whereas the number of distractors may only interfere with early character selection, but not with updating and maintenance phases.

Enhancing scene text detectors with realistic text image synthesis using diffusion models

Scene text detection techniques have garnered significant attention due to their wide-ranging applications. However, existing methods have a high demand for training data, and obtaining accurate human annotations is labor-intensive and time-consuming. As a solution, researchers have widely adopted synthetic text images as a complementary resource to real text images during pre-training. Yet there is still room for synthetic datasets to enhance the performance of scene text detectors. We contend that one main limitation of existing generation methods is the insufficient integration of foreground text with the background. To alleviate this problem, we present the Diffusion Model based Text Generator (DiffText), a pipeline that utilizes the diffusion model to seamlessly blend foreground text regions with the background’s intrinsic features. Additionally, we propose two strategies to generate visually coherent text with fewer spelling errors. With fewer text instances, our produced text images consistently surpass other synthetic data in aiding text detectors. Extensive experiments on detecting horizontal, rotated, curved, and line-level texts demonstrate the effectiveness of DiffText in producing realistic text images. Code will be available at: https://github.com/99Franklin/DiffText.

EGO-LM: An efficient, generic, and out-of-the-box language model for handwritten text recognition

EGO-LM: An efficient, generic, and out-of-the-box language model for handwritten text recognition

Navigating the Landscape of AI-Generated Text Detection: Issues and Solutions for Upholding Academic Integrity

Navigating the Landscape of AI-Generated Text Detection: Issues and Solutions for Upholding Academic Integrity