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Off-line Bangla handwritten word recognition: a holistic approach

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Abstract

Due to the cursive nature, segmentation of handwritten Bangla words into characters and also recognition of the same sometimes become a very challenging problem to the researchers. Presence of comparatively large character set along with modifiers, ascendants, descendants, and compound characters makes the segmentation task more complex. As holistic method avoids such character-level segmentation, it is generally useful for the recognition of words written in any such complex scripts. In the present work, a holistic handwritten word recognition method is developed using a feature descriptor, designed by combining different Elliptical, Tetragonal and Vertical pixel density histogram-based features. Recognition process is carried out separately using two classifiers, namely multi-layer perceptron (MLP) and support vector machine (SVM). For evaluation of the proposed method, a database of 18,000 handwritten Bangla word images, having 120 word classes, is prepared. The proposed system performs comparatively better with SVM than MLP for the prepared dataset. It has achieved 83.64% accuracy at best case and 79.38% accuracy on an average using fivefold cross-validation. The current method has also outperformed some recently reported holistic word recognition technique tested on the developed dataset. In addition to that the database, prepared in this work, is made freely available to fill the absence of a publicly available standard database for holistic Bangla word recognition.

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Notes

  1. By foreground pixels in a word image, we mean object pixels only and the rest of the pixels are considered as representing the background. In this paper, we have followed this convention.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Showmik Bhowmik, Ram Sarkar, Subhadip Basu, Mahantapas Kundu & Mita Nasipuri

  2. Department of Computer Science, Asutosh College, Kolkata, India

    Samir Malakar

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  1. Showmik Bhowmik
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  2. Samir Malakar
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  3. Ram Sarkar
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  4. Subhadip Basu
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  5. Mahantapas Kundu
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  6. Mita Nasipuri
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Correspondence to Samir Malakar.

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Bhowmik, S., Malakar, S., Sarkar, R. et al. Off-line Bangla handwritten word recognition: a holistic approach. Neural Comput & Applic 31, 5783–5798 (2019). https://doi.org/10.1007/s00521-018-3389-1

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