Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Amir Hussain,et al.  Persian Named Entity Recognition , 2017, 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[3]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Amir Hussain,et al.  Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System , 2020, INTERSPEECH.

[5]  Jingpeng Li,et al.  A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks , 2019, Neurocomputing.

[6]  Mohamed E. M. Musa.  Arabic handwritten datasets for pattern recognition and machine learning , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[7]  Kavita R. Singh,et al.  A technical review on text recognition from images , 2015, 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO).

[8]  Hazem M. El-Bakry,et al.  Arabic Handwritten Characters Recognition Using Convolutional Neural Network , 2017 .

[9]  Amir Hussain,et al.  Lip-Reading Driven Deep Learning Approach for Speech Enhancement , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[10]  Mohamed Elhafiz Mustafa,et al.  A Deep Learning Approach for Handwritten Arabic Names Recognition , 2020 .

[11]  Hadi Larijani,et al.  Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection , 2018, BICS.

[12]  Amir Hussain,et al.  A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[13]  Kia Dashtipour.  Novel symbolic and sub-symbolic approaches for text based and multimodal sentiment analysis , 2019 .

[14]  Mauricio Marengoni,et al.  A Survey of Transfer Learning for Convolutional Neural Networks , 2019, 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T).

[15]  Muhammad Tariq,et al.  Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing , 2019, Cognitive Computation.

[16]  Monji Kherallah,et al.  Convolutional Neural Network and BLSTM for Offline Arabic Handwriting Recognition , 2018, 2018 International Arab Conference on Information Technology (ACIT).

[17]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[18]  E. Sreenivasa Reddy,et al.  Role of Offline Handwritten Character Recognition System in Various Applications , 2016 .

[19]  Liangrui Peng,et al.  Multi-layer recurrent neural network based offline Arabic handwriting recognition , 2017, 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR).

[20]  Mounir Ait Kerroum,et al.  A review of feature extraction techniques for handwritten Arabic text recognition , 2015, 2015 International Conference on Electrical and Information Technologies (ICEIT).

[21]  Hadi Larijani,et al.  A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management , 2018, Springer Natural Hazards.

[22]  Shadi Aljawarneh,et al.  Hybrid modeling of an OffLine Arabic Handwriting Recognition System AHRS , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[23]  Imran Siddiqi,et al.  A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation , 2015, EURASIP J. Image Video Process..

[24]  Mohammad S. Khorsheed,et al.  Off-Line Arabic Character Recognition – A Review , 2002, Pattern Analysis & Applications.

[25]  Chokri Ben Amar,et al.  Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[26]  Saad M. Darwish,et al.  A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm , 2019, AISI.

[27]  Hazem M. El-Bakry,et al.  CNN for Handwritten Arabic Digits Recognition Based on LeNet-5 , 2016, AISI.

[28]  Mounir Ait Kerroum,et al.  Word-based Arabic handwritten recognition using SVM classifier with a reject option , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[29]  Venu Govindaraju,et al.  Offline Arabic handwriting recognition: a survey , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Tianbao Yang,et al.  Improved Dropout for Shallow and Deep Learning , 2016, NIPS.

[31]  Monji Kherallah,et al.  Offline Arabic Handwritten recognition system with dropout applied in Deep networks based-SVMs , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[32]  Mengjie Zhang,et al.  Completely Automated CNN Architecture Design Based on Blocks , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Mohamed Batouche,et al.  Investigation on deep learning for off-line handwritten Arabic character recognition , 2017, Cognitive Systems Research.

[34]  Ausif Mahmood,et al.  A Framework for Designing the Architectures of Deep Convolutional Neural Networks , 2017, Entropy.

[35]  Said Raghay,et al.  Comparative systems of handwriting Arabic character recognition , 2014, 2014 Second World Conference on Complex Systems (WCCS).

[36]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[38]  Jawad H. AlKhateeb,et al.  A Database for Arabic Handwritten Character Recognition , 2015 .

[39]  Qiang Zhou,et al.  PerSent: A Freely Available Persian Sentiment Lexicon , 2016, BICS.

[40]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Syed Aziz Shah,et al.  A Review of the State of the Art in Non-Contact Sensing for COVID-19 , 2020, Sensors.

[42]  Zain U. Hussain,et al.  Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations , 2020, medRxiv.

[43]  Kaizhu Huang,et al.  Random Features and Random Neurons for Brain-Inspired Big Data Analytics , 2019, 2019 International Conference on Data Mining Workshops (ICDMW).

[44]  Bo Li,et al.  Data Dropout: Optimizing Training Data for Convolutional Neural Networks , 2018, 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI).

[45]  A. Hussain,et al.  Towards Next-Generation Lip-Reading Driven Hearing-Aids : A preliminary Prototype Demo , 2017 .

[46]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[47]  Erik Cambria,et al.  Semi-supervised learning for big social data analysis , 2018, Neurocomputing.

[48]  Jingpeng Li,et al.  Robust Visual Saliency Optimization Based on Bidirectional Markov Chains , 2020, Cognitive Computation.

[49]  Monji Kherallah,et al.  Deep Learning for Feature Extraction of Arabic Handwritten Script , 2015, CAIP.

[50]  Giuseppe Pirlo,et al.  A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia , 2019, Cognitive Computation.

[51]  Aleksander Madry,et al.  How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) , 2018, NeurIPS.

[52]  Houssem Lahiani,et al.  Recognizing Arabic Handwritten Script using Support Vector Machine classifier , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[53]  Zeeshan Shaukat,et al.  A robust CNN model for handwritten digits recognition and classification , 2020, 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA).

[54]  Jenny Benois-Pineau,et al.  Increasing Training Stability for Deep CNNS , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[55]  Xue Li,et al.  An Effective Approach to Offline Arabic Handwriting Recognition , 2013, Comput. Electr. Eng..

[56]  L. Myers.  Aliterate community college remedial students and their attitudes toward reading: a phenomenological examination , 2013 .

[57]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[58]  Abdul Kawsar Tushar,et al.  Handwritten Arabic numeral recognition using deep learning neural networks , 2017, 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR).

[59]  Mohamed Saber Naceur,et al.  Reinforcement learning for neural architecture search: A review , 2019, Image Vis. Comput..

[60]  Alexander F. Gelbukh,et al.  Adaptation of Sentiment Analysis Techniques to Persian Language , 2017, CICLing.

[61]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Aasim Zafar,et al.  Offline Handwritten Quranic Text Recognition: A Research Perspective , 2019, 2019 Amity International Conference on Artificial Intelligence (AICAI).

[63]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[64]  Jon Barker,et al.  DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation , 2018, INTERSPEECH.

[65]  Monji Kherallah,et al.  Towards Unsupervised Learning for Arabic Handwritten Recognition Using Deep Architectures , 2015, ICONIP.

[66]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[67]  Hazem M. El-Bakry,et al.  CNN for Handwritten Arabic Digits Recognition Based on LeNet-5 , 2016, AISI.

[68]  Amir Hussain,et al.  Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect , 2018, BICS.

[69]  Mouhcine Rabi,et al.  Convolutional Feature Learning and CNN Based HMM for Arabic Handwriting Recognition , 2018, ICISP.

[70]  M. N. Sulaiman,et al.  A Review On Evaluation Metrics For Data Classification Evaluations , 2015 .

[71]  Amir Hussain,et al.  Deep Neural Network Driven Binaural Audio Visual Speech Separation , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[72]  Ashiqur Rahman,et al.  An Efficient Recognition Method for Handwritten Arabic Numerals Using CNN with Data Augmentation and Dropout , 2018, Data Management, Analytics and Innovation.

[73]  Peter Derleth,et al.  AV Speech Enhancement Challenge using a Real Noisy Corpus , 2019, ArXiv.

[74]  Amir Hussain,et al.  Deep learning driven multimodal fusion for automated deception detection , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[75]  Kaizhu Huang,et al.  Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances , 2019, BICS.

[76]  Amir Hussain,et al.  A Survey on Deep Learning for the Routing Layer of Computer Network , 2019, 2019 UK/ China Emerging Technologies (UCET).

[77]  Monji Kherallah,et al.  Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition , 2019, Int. J. Multim. Data Eng. Manag..

[78]  Hadi Larijani,et al.  Exploiting Deep Learning for Persian Sentiment Analysis , 2018, BICS.

[79]  Syed A. R. Abu-Bakar,et al.  A database of Arabic handwritten characters , 2014, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014).

[80]  Ahsan Adeel,et al.  CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement , 2019, Inf. Fusion.

[81]  R. Lakshmi,et al.  Offline handwritten character classification of the same scriptural family languages by using transfer learning techniques , 2020, 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE).

[82]  Tariq S. Durrani,et al.  Toward's Arabic Multi-modal Sentiment Analysis , 2017, CSPS.

[83]  Hong Qiao,et al.  Guided Policy Search for Sequential Multitask Learning , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[84]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[85]  Huihuang Zhao,et al.  Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition , 2020, Granular Computing.

[86]  Venu Govindaraju,et al.  Knowledge Transfer Using Neural Network Based Approach for Handwritten Text Recognition , 2018, 2018 13th IAPR International Workshop on Document Analysis Systems (DAS).

[87]  Khaled S. Younis,et al.  Arabic Hand-Written Character Recognition Based on Deep Convolutional Neural Networks , 2017 .

[88]  Amit Choudhary,et al.  Hybrid CNN-SVM Classifier for Handwritten Digit Recognition , 2020 .

[89]  Dmitrii Tumakov,et al.  Hierarchical Convolutional Neural Network for Handwritten Digits Recognition , 2020 .

[90]  Francesco Carlo Morabito,et al.  An Ensemble Based Classification Approach for Persian Sentiment Analysis , 2017, IIH-MSP.

[91]  Erik Cambria,et al.  PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words , 2019, BICS.

[92]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[93]  Katarzyna Stapor,et al.  Evaluating and Comparing Classifiers: Review, Some Recommendations and Limitations , 2017, CORES.

[94]  Hercules Dalianis,et al.  Evaluation Metrics and Evaluation , 2018 .

[95]  Monji Kherallah,et al.  A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition , 2016, ICCS.

[96]  Mohamed Elhafiz Mustafa,et al.  Convolutional Neural Network Model for Arabic Handwritten Characters Recognition , 2018, IJARCCE.

[97]  Sridha Sridharan,et al.  Improving deep convolutional neural networks with unsupervised feature learning , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[98]  Monji Kherallah,et al.  Arabic handwritten characters recognition using Deep Belief Neural Networks , 2015, 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15).

[99]  M. A. Mohamad,et al.  A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition , 2015 .

[100]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[101]  Ghazanfar Latif,et al.  Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals , 2018, 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR).