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Novel Adaptive Binarization Method for Degraded Document Images

Computers, Materials & Continua 2021年第6期67卷 3815-3832页

作者： siti norul huda Sheikh abdullah Saad M.Ismail Mohammad Kamrul Hasan Palaiahnakote Shivakumara Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaSelangor43600Malaysia The Faculty of Computer and Information Sciences Aljouf UniversitySaudi Arabia Faculty of Computer Systems and Information Technology University of MalayaMalaysia

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast,bleedthrough,and nonuniform illumination *** the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better *** enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced *** the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic *** dynamic window is derived by exploiting the advantage of Otsu *** assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for *** comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

关键词： Global and local thresholding adaptive binarization degraded document image image histogram document image binarization contest

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Text Extraction with Optimal Bi-LSTM

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Computers, Materials & Continua 2023年第9期76卷 3549-3567页

作者： Bahera H.Nayef siti norul huda Sheikh abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed Computer Techniques Engineering Department Ibn Khaldun University CollegeBaghdad10011Iraq Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University MalaysiaSepang43900Malaysia

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the *** Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant *** study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain *** pass them to deep learning layers to extract feature maps and reform the predicted *** Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training *** Connectionist Temporal Classification(CTC)function calcifies the training and validation loss *** addition to decoding the extracted feature to reform characters again and linking them according to their time *** proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)*** result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and *** the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

关键词： Deep neural network text features dual max-pooling concatenating convolution neural networks bidirectional long short memory text connector characteristics

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IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

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Computers, Materials & Continua 2022年第5期71卷 2579-2597页

作者： Taher M.Ghazal Mohammad Kamrul Hasan siti norul huda abdullah Khairul Azmi Abubakkar Mohammed A.M.Afifi Faculty of Information Science and Technology University Kebangsaan MalaysiaUKM43600SelangorMalaysia Skyline University College SharjahUnited Arab Emirates

Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring *** systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic *** collection of WS data and integration of that data for diagnostic purposes is a difficult *** paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition *** research is based on a case study in a health *** the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized *** a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic *** breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect *** paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance *** of WS data are examined extensively using active and iterative learning to identify errors in specific *** technology improves position detection accuracy,analysis duration,and error rate,regardless of user *** devices play a critical role in the management and treatment of *** can ensure that patients are provided with a unique treatment for their medical *** paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature *** first,the patients’walking patterns are tracked at various time *** characteristics are then evaluated in relation to the stored data using a random forest classifier.

关键词： Data fusion(DF) posture recognition healthcare systems(HCS) wearable sensor(WS) medical data errorless data fusion(EDF)

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