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    Research report on Bengla OCR training and testing methods

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    Research report on Bangla OCR training and testing methods.pdf (205.0Kb)
    Date
    2007
    Publisher
    BRAC University
    Author
    Hasnat, Md. Abul
    Metadata
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    URI
    http://hdl.handle.net/10361/657
    Abstract
    In this paper we present the training and recognition mechanism of a Hidden Markov Model (HMM) based multi-font Optical Character Recognition (OCR) system for Bengali character. In our approach, the central idea is to separate the HMM model for each segmented character or word. The system uses HTK toolkit for data preparation, model training and recognition. The Features of each trained character are calculated by applying the Discrete Cosine Transform (DCT) to each pixel value of the character image where the image is divided into several frames according to its size. The extracted features of each frame are used as discrete probability distributions which will be given as input parameters to each HMM model. In the case of recognition, a model for each separated character or word is built up using the same approach. This model is given to the HTK toolkit to perform the recognition using the Viterbi Decoding method. The experimental results show significant performance over models using neural network based training and recognition systems.
    Keywords
    Bangla language processing; Bangla OCR
     
    Description
    Includes bibliographical references (page 6-7).
    Department
    Center for Research on Bangla Language Processing (CRBLP), BRAC University
    Collections
    • Technical Papers (Centre for Research on Bangla Language Processing)

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