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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 11

Multi-Scale Features Integration for Handwritten Mathematica...

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11th international conference on computing and pattern recognition, ICCPR 2022

作者： Liu, Xianghao Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method. © 2022 ACM.

关键词： Character recognition

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IET conference Proceedings

IET Conference Proceedings

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8th international conference on computing in Engineering and Technology, ICCET 2023

ISBN: (纸本)9781839539237

The proceedings contain 85 papers. The topics discussed include: performance analysis of radial system with renewable sources;artificial intelligence framework for rice blast disease detection and classification using recurrent neural networks;road anomaly detection using self-supervised label generator with vertical disparity maps;sEMG signal analysis and classification for precision and pulp to pulp grip recognition;Internet of Things and robotics: a review of concept, added value and applications;a deep learning based approach to enhance the Quality of Service (QoS) of next generation wireless systems by large intelligent surfaces;new applications of morphological methodology for the early detection of cancer;IoT based automatic fuel level monitoring system for automobiles;evolutionary technique for high utility sequential pattern mining;high utility sequence mining using cuckoo search algorithm;image restoration and object detection in unfavorable weather conditions for autonomous vehicles using deep learning approaches: a review;and a review of image super-resolution approaches based on deep learning methods.

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License Plate Detection using YOLO v8 and Performance Evaluation of EasyOCR, PaddleOCR and Tesseract

License Plate Detection using YOLO v8 and Performance Evalua...

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international conference on computing and Networking Technology (ICCNT)

作者： P Praneeth Reddy P Sai Shruthi P Himanshu Tripty Singh Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

License plate recognition is critical to the functioning of the control and surveillance systems. Vehicle identification is based on number plates, which are made up of a certain arrangement of letters and digits. However, manually identifying every number plate is a laborious and difficult task. The majority of earlier automatic number plate recognition (ANPR) systems were restricted in how they could operate. These conditions included being limited to fixed illumination, background noise in the vehicle image. The primary goal of this project is to develop a reliable number plate recognition model that functions with the help of YOLO v8 for license plate segmentation followed with a comparison model of EasyOCR, PaddleOCR, Tesseract models based on best model for character recognition of license plate. The steps involved include processing of vehicle image, segmentation of license plate from the image, followed by internal segmentation of characters on the plate and pattern matching. Data pre-processing is finally performed in order to read and process the license plate. Problems like background noise in image like characters apart from the license plate and number-based characters being wrongly predicted as alphabets have been tackled.

关键词： YOLO Performance evaluation Image segmentation Surveillance Lighting Control systems Reliability Background noise License plate recognition pattern matching

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Enhancing Barangay Safety: Leveraging Convolutional Neural Networks for Comprehensive Detection of Brawling Incidents in Philippine Barangays

Enhancing Barangay Safety: Leveraging Convolutional Neural N...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： John Clement S. Escobañez Mark James G. Cayabyab Julie Anne Angeles-Crystal College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Street Brawling remains a significant concern within the Philippine and global context, posing a threat to community well-being. While extensively recognized as a major issue affecting the younger generation worldwide, the focus on such incidents has predominantly centered around schools. However, in the barangay setting, brawling incidents pose a distinct challenge that demands attention. This paper delves into the realm of action recognition through Machine Learning, aiming to contribute to the proactive identification and understanding of physical violence within the community. Employing Convolutional Neural Networks, the study focuses on training algorithms to detect actions associated with brawling incidents, including punching, and head hitting. The positive outcomes underscore the effectiveness of Convolutional Neural Networks in recognizing these actions. Leveraging CCTV cameras, the findings present an avenue for preventing and addressing brawling incidents within the barangay, ultimately fostering a safer and more secure community environment.

关键词： Training Accuracy Punching Cameras Magnetic heads pattern recognition Safety Convolutional neural networks Object recognition Testing

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Multi-Scale Channel Attention for Chinese Scene Text recognition 11

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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11th international conference on computing and pattern recognition, ICCPR 2022

作者： Liao, Hai-Qing Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. The experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches. © 2022 ACM.

关键词： Character recognition

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An Improved soft-CBAM-YoloV5 Algorithm for Fruits and Vegetables Detection and Counting

An Improved Soft-CBAM-YoloV5 Algorithm for Fruits and Vegeta...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Qian Luo Zhen Zhang Cuimei Yang Junran Lin Department of Soft Engineering South China Normal University Foshan Department of Soft Engineering Huizhou University Huizhou

The quality of people's life has been improved as science and technology keep progressing, and artificial intelligence has been implemented into more and more fields these days. Among them, many researchers have paid attention to the scenes of quoting prices and settling accounts for fruits and vegetables in supermarkets and manual classification of fruits and vegetables in farmers' markets. In the traditional YOLOV5 method, when the same kind of fruits and vegetables are blocked from each other, the counting will be inaccurate, resulting in missed detection. In addition, the environment where different objects stay will affect the feature extraction of objects. In order to reduce the occurrence of missed detections as much as possible and improve the detection performance of the model, this paper proposes a new detection model counting method named soft-CBAM-YOLOV5, by reasonably reducing the score of adjacent detection boxes and introducing the attention mechanism CBAM (Convolutional Block Attention Module) to prevent the occurrence of missed detection and strengthen the focus on the detection target to improve the feature extraction ability of the algorithm. Compared with traditional YOLOV5, the mAP value of soft-CBAM-YOLOV5 increases from 97.05% to 97.71%. Consequently, the soft-CBAM-YOLOV5 model has greater detection performance, robustness and lower missed detection rate.

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FPGA-Based Automated EEG Analysis for Efficient Emotion Detection

FPGA-Based Automated EEG Analysis for Efficient Emotion Dete...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： P Vinayagam P Kishore Kumar M Kandhan Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The recognition of emotions has significant importance in the domains of human-computer interaction and affective computing. The integration of facial expressions, voice analysis, and EEG data in multimodal techniques has shown potential in improving the accuracy of emotion identification. In this study, we provide a novel approach for emotion identification by incorporating facial expression analysis, voice processing, and EEG data analysis into a multimodal system. Machine learning methods are used by the system to classify emotions. The findings of our study indicate that the suggested multimodal technique yields a notable level of classification accuracy across many emotion categories, namely Happy (85%), Sad (75%), Angry (80%), and Neutral (70%). In conclusion, the incorporation of many modalities has been shown to improve the accuracy of emotion identification in comparison to unimodal methodologies. This study highlights the efficacy of multimodal emotion detection systems in capturing a wide range of emotional states, hence facilitating advancements in human-computer interaction and affective computing applications.

关键词： Emotion recognition Affective computing System performance soft sensors Scalability Electroencephalography Real-time systems

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Localization of Focal Intractable Epileptic Focus Based on Long-Term Recurrent Convolutional Network

Localization of Focal Intractable Epileptic Focus Based on L...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Zhen Zhang Qian Luo Yi Zhou Department of Soft Engineering Huizhou University Huizhou China Department of Soft Engineering South China Normal University Foshan China Department of Biomedical Engineering Sun Yat-sen University Guangzhou China

In clinic, the success rate of surgical resection of the epileptic focus in the treatment of focal intractable epilepsy is only about 25%. The main reason for the low success rate is that the location of epileptic focal is difficult to accurately determine. Although some methods have built models to automatically identify the location of epileptic focal according to the characteristic differences between focal electroencephalogram (EEG) and non-focal EEG, the recognition accuracy of previous methods needs to be further improved. We note that the previous methods only consider the spatial differences and do not take the variation differences in the time domain into account. In this study, we intend to combine the convolution neural network and the long short-term memory neural network to construct a time-space deep learning model (long-term recurrent convolutional network, LRCN) to improve the accuracy of EEG recognition of epileptic focal. Based on the LRCN model, we achieved an accuracy of 97.13% for epileptic focal recognition, which performed out the existing methods.

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing soft computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Vehicle Theft Detection Using Neural pattern recognition

Vehicle Theft Detection Using Neural Pattern Recognition

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Pervasive computing and Social Networking (ICPCSN), international conference on

作者： J. Jane Rubel Angelina S.J Subhashini Anil Kumar Rapuri Vennela Ravva Raghavendra Rasala Sai Dcepak Ray Adurgam Dept of Computer Science and Engineering Kalasalingam Academy Of Research And Education Krishnankoil TamilNadu India Dept of Computer Science and Engineering SRM Madurai College for Engineering and Technology Sivagangai TamilNadu India

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge.

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