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  • Open Access


    Decision Support System Tool for Arabic Text Recognition

    Fatmah Baothman*, Sarah Alssagaff, Bayan Ashmeel

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 519-531, 2021, DOI:10.32604/iasc.2021.014828

    Abstract The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative… More >

  • Open Access


    An Attention-Based Recognizer for Scene Text

    Yugang Li1, *, Haibo Sun1

    Journal on Artificial Intelligence, Vol.2, No.2, pp. 103-112, 2020, DOI:10.32604/jai.2020.010203

    Abstract Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. Although STR method has been greatly developed, the existing methods still can't recognize any shape of text, such as very rich curve text or rotating text in daily life, irregular scene text has complex layout in two-dimensional space, which is used to recognize scene text in the past Recently, some recognizers correct irregular text to regular text image with approximate 1D layout, or convert 2D image feature mapping to one-dimensional feature sequence. Although these methods have achieved good performance, their robustness and accuracy are limited due… More >

  • Open Access


    A Novel Scene Text Recognition Method Based on Deep Learning

    Maosen Wang1, Shaozhang Niu1,*, Zhenguang Gao2

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 781-794, 2019, DOI:10.32604/cmc.2019.05595

    Abstract Scene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition task of sequence texts. Our… More >

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