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Text recognition in Persian Documents Using Self-Supervised learning

Text Recognition in Persian Documents Using Self-Supervised ...

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international Image Processing, Applications and Systems conference (IPAS)

作者： Atefeh Babaei Alireza Dehghani Mehran Yazdi Ali Jamshidi Mahdi Rasouli School of Electrical and Computer Engineering Shiraz University Shiraz Iran CeADAR Ireland's Centre For AI Dublin Ireland School of Computer Science and Mathematics Université Grenoble Alpes Grenoble France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like Persian, presents unique challenges due to issues such as font types, word structures, and the scarcity of labeled data. this paper addresses these challenges by implementing deep learning techniques, specifically self-supervised learning with convolutional and recurrent neural networks (CNNs and RNNs). Our approach successfully mitigates the data scarcity problem, achieving an accuracy of 92.9% on the prepared dataset.

关键词： Deep learning Technological innovation Recurrent neural networks Accuracy Text recognition Image processing Collaboration Self-supervised learning data mining Convolutional neural networks

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Secured Challan Filling Automation Using OCR Technology and machine learning

Secured Challan Filling Automation Using OCR Technology and ...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Adhithya Sivakumar Karthick Mohan Gunasekar Elango Nithya Jayakumar Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the Automated Challan Filling System integrates several advanced technologies, such as Optical Character Recog-nition (OCR), machine learning (ML), and voice assistance to modernize the bank challan process. Such technologies reduce manual entries and errors in the physical forms by automatically extracting key data from them. ML helps validate the accuracy of data in addition to detecting false cases for enhanced se-curity. the option of multiple authentication methods such as PIN, OTP, and biometric avenues ensures secure, personalized transactions. With a voice assistant that guides users step-by-step, it makes the system user-friendly, especially to literate and non-literate and low-tech users. Multilingual and with a user-friendly interface, the system enhances accessibility, efficiency, and transaction speed in addressing the disadvantaged users' challenges in accessing traditional banking processes.

关键词： Integrated optics Biomedical optical imaging Text recognition Optical character recognition Personal voice assistants machine learning Filling Adaptive optics Pins Multilingual

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A Survey on machine learning Techniques for Recognizing Human Activities Using Smartphone data 6th

A Survey on Machine Learning Techniques for Recognizing Huma...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Das, Prodipto Singh, Tonjam Rahul Assam University Silchar India

ISBN: (纸本)9783031813412

the increasing ubiquity of smartphones has led to the generation of vast amounts of data related to human activities. Human activity recognition is an important area of machine learning research as it has many applications, including entertainment, smart homes, smart environments, healthcare, security, etc. the research direction for HAR is shifting towards using smartphones because of their reliability in collecting and processing data independently using different embedded sensors. this paper displays the classification of HAR, sensors used in smartphones, classification of daily living activities, different approaches to machine learning techniques: traditional machine learning and deep learning (supervised and unsupervised), available datasets for HAR, and data processing for accurately classifying different human activities. through an extensive literature review, this paper compares and displays many human activity recognition systems implemented on smartphone data or publicly available datasets using different sensors regarding the dataset used, classifiers, sensor used, advantages, disadvantages, and recognition accuracy in percentage. the paper also displays the comparisons of the best five papers in terms of author, year, dataset, algorithm and their accuracy, and the challenges of HAR. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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RADA: Reconstruction Assisted Domain Adaptation for Nighttime Aerial Tracking 27th

RADA: Reconstruction Assisted Domain Adaptation for Nightti...

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27th international conference on pattern recognition, ICPR 2024

作者： Chouhan, Avinash Chandak, Mayank Sur, Arijit Chutia, Dibyajyoti Aggarwal, Shiv Prasad North Eastern Space Applications Centre Umiam793103 India Indian Institute of Technology Guwahati Assam Guwahati781039 India

ISBN: (纸本)9783031781919

Visual object tracking is a popular research area in computer vision due to its diverse applications. Despite the impressive progress made by numerous state-of-the-art trackers on large-scale datasets, visual object tracking at nighttime remains challenging because of low light (brightness) conditions, lack of contrast, very low variability among feature distributions, etc. In addition, the lack of paired (labeled) data for nighttime tracking makes it infeasible for supervised learning based modeling. Unsupervised domain adaptation based tracking can resolve this issue. In this work, we proposed static image style transfer-based Reconstruction Assisted Domain Adaptation (RADA) with adversarial learning for nighttime object tracking. the main contribution of the work is two-fold. First, a reconstruction-assisted adaptation is proposed for domain invariant feature extraction and to achieve input and feature level adaptation. Secondly, static style transfer is used to generate synthetic paired images (video frames) for supervised nighttime modeling for visual object tracking. Style adversarial alignment at multiple levels helped to adapt between the styled source domain and the target domain, which do not require pseudo labels. RADA attained feature and input level adaptation without external model requirements for low-light image enhancement. Static style transfer avoids negative domain transfer and enables domain transfer learning on true labels. the effectiveness of RADA is validated on six benchmark datasets. RADA achieved state-of-the-art results on two benchmark nighttime adaptation datasets with improvements in the range of 3.7% - 11.4%. RADA also attained state-of-the-art results on three other nighttime datasets without target adaptation. the tracking results and model weights are available at https://***/chouhan-avinash/RADA/. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

6th International Conference on Advanced Informatics for Com...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

ISBN: (纸本)9783031840616

the proceedings contain 58 papers. the special focus in this conference is on Advanced Informatics for Computing Research. the topics include: Face Spoof Detection: the Preprocessing Paradigm;Traffic Sign recognition Using Convolutional Neural Networks with Transfer learning for Advanced Driving Assistance Systems (ADAS);analyzing the Criticalness of "the Silent Killer" Using machine learning;AI-Optimized Erection of Landslide-Resistant Retaining Structures through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough;do Software Refactoring Reflects Developers’ Perception About Software’s Code Smells and Its Quality: An Empirical Study;Transfer learning for Pneumonia Classification from DICOM Medical Imaging data;A Study to Explore AI Techniques Used for Analyzing the Impact and Severity of Detrimental Human Health Effects Caused by Agrochemicals;Distance Metric Selection for AIS data Clustering Using DBSCAN and ST-DBSCAN;fake News Detection and Classification Using Deep learning Techniques for Social Media Text;real-Time Resource Allocation for Electric Vehicle Charging Stations Using Reinforcement learning;data Sciences: Cutting Edge Tools and Techniques;Exploratory data Analysis of Phishing URL Detection Using Gradient Boosting Classifier;advancing Healthcare Systems with Low-Power and Efficient Double Tail Dynamic Latch Comparator;SAMBHAV: Socially Sensed, Accessible, Mobile, Behaviour-Based and Human-Centric Smart Wearable Devices Aimed for Visually Impaired and Persons with Allied Disabilities;multi-disease Classification and Prediction Using Dense Visual Attention Network and Residual Network for: A Computer Vision Approach;Enhancing Text Extraction Using OCR with AWS Textract Service;a Study: recognition of Facial Emotions Using Deep learning;machine learning-Based Systems for Diagnosing Multiple Diseases and Providing Dietary Recommendations;machine learning Approaches for Evaluation of Chronic Kidney Disease.

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

6th International Conference on Advanced Informatics for Com...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

ISBN: (纸本)9783031840586

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OSOL: Open Linked data Generation Using Semantics Oriented learning Paradigms 15th

OSOL: Open Linked Data Generation Using Semantics Oriented L...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is an imminent need for a strategic framework for open linked data and meta tag generation for web pages as they would be useful in indexing which would serve as a roadmap for retrieval and also several other applications such as text summarization, text mining, etc. this paper proposes an open-linked data generation framework in which the XGBoost classifier classifies the web URL dataset. Category extraction and URL canonicalization are performed which helps in strategic maximal utilization of the term set. Strategic knowledge base repositories like CYC and NELL are used for entity enrichment in the model. the RBFNN classifies the web-based directories extracted from the current structure of the web using a focused crawler. Unlike most traditional frameworks, the OSOL uses Semantics-oriented reasoning, which is achieved through the SimRank measure with an empirically decided threshold. the Eagle optimization algorithm (a metaheuristic, nature-inspired algorithm) generates the optimal solution set. Shannon’s entropy helps in feature selection for classifying the dataset. this framework can achieve an overall mean precision percentage of 95.17%, a mean recall percentage of 96.75%, and an FDR value of 0.05, making it the best-in-class model for linked open data and meta-tag generation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge engineering

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. the developed approach uses image processing and machine learning tools to correctly extract and classify the features. the framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. the proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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Classification of Teas Using machine and Deep learning Methods on a Custom e-nose Platform 15th

Classification of Teas Using Machine and Deep Learning Metho...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Jiménez-López, Irari Quiroga, Jeniffer Molina Garduño, Luis Fernando Valdez Gutiérrez, Juan Manuel Bioelectronics Section Department of Electrical Engineering CINVESTAV-IPN Mexico City07360 Mexico

ISBN: (纸本)9783031810855

Tea and herbal infusions are significant to human life because they possess many medical benefits such as antioxidant and anti-inflammatory properties, among others;for that reason, the identification of the tea class is crucial to fulfill this task. Some analytical methods are used to describe the chemical composition, nonetheless, they are costly, time-consuming and require sophisticated laboratory training. Novel devices like electronic noses (e-noses) offer real-time and objective odor monitoring to identify tea types. the aim of this work is to determine which kind of tea (among 9 known classes) a sample belongs based on volatile organic compounds (VOCs) e-nose detection using two models of artificial intelligence: support vector machine (SVM) and convolutional neural network (CNN). A total of 59 tea samples were analyzed by a lab-made e-nose consisting of an olfactometer, seven metal-oxide semiconductor (MOS) gas sensors, and a 12-bit analog-to-digital converter. Each tea was sampled ten times to ensure repeatability, obtaining a database of 590 tea measures with 2499 samples per sensor. the data was modelled using machine learning (ML) and deep learning (DL) techniques where DL can classify without a previous feature extraction. the SVM model employed principal component analysis as a feature extraction method. Using 10-fold cross-validation, the capability of both models was assessed. the classification rate was ca. 96.4% in both artificial intelligence (AI) methods, demonstrating that DL models are cost-effective and efficient. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Volatile organic compounds

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An Innovative Method for Detecting Brain Tumors that Combines Artificial Intelligence (AI), machine learning (ML), and Deep learning with Artificial data Augmentation

An Innovative Method for Detecting Brain Tumors that Combine...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Narenthirakumar Appavu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Effective treatment and better patient outcomes are made possible by early brain tumor identification. Traditional diagnostic techniques are trustworthy, but they frequently depend on unreliable and time-consuming manual examination. In order to improve brain tumor diagnosis from MRI images, this work combines HOG the Histogram of Oriented Gradients (HOG)HOG for extracted features, HOG Support Vector machine (SVM)HOG clustering over segmentation, HOG the ResNet50HOG for deep learning-mediated classification, including HOG based data augmentation HOG. SVM clustering overcomes the drawbacks of linear clustering techniques by efficiently segmenting tumor zones. Texture-based characteristics that offer useful information for classification, such contrast and homogeneity, are extracted using HOG. the ResNet50 model fine-tunes its layers according to the retrieved characteristics, delivering great accuracy thanks to its strong deep learning architecture. By mitigating data scarcity, synthetic data augmentation ensures the generalizability and robustness of the model. Results from experiments on publically accessible datasets show notable gains in specificity, accuracy, and efficiency, underscoring the usefulness of this strategy in offering dependable and expandable automated detection of brain tumors systems.

关键词： Support vector machines Deep learning Image segmentation Accuracy Brain tumors Feature extraction data augmentation Brain modeling data mining Residual neural networks

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