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Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces
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Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks
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Image Classification Using TensorFlow
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Maxout into MDLSTM for Offline Arabic Handwriting Recognition
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Deep Neural Networks Features for Arabic Handwriting Recognition
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Convolutional Feature Learning and CNN Based HMM for Arabic Handwriting Recognition
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Online versus offline Arabic script classification
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Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models
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Convolutional Neural Network and BLSTM for Offline Arabic Handwriting Recognition
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Histograms of Oriented Gradients for Human Detection
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Going deeper with convolutions
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CNN-N-Gram for HandwritingWord Recognition
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Residual Recurrent Neural Network with Sparse Training for Offline Arabic Handwriting Recognition
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Early feature stream integration versus decision level combination in a multiple classifier system for text line recognition
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HMMs with Explicit State Duration Applied to Handwritten Arabic Word Recognition
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Likhai Database Urdu Handwritten Text Using Sampling Strategies
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A Comparative Analysis of Two Arabic Handwritten Datasets Using Pretrained Deep Learning Models
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