Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition

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M. A. H. Akhand 1,* Mahtab Ahmed 1 M. M. Hafizur Rahman 2

1. Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh

2. Dept. of Computer Science, KICT, International Islamic University Malaysia (IIUM) Selangor, Malaysia

* Corresponding author.


Received: 7 Jun. 2016 / Revised: 6 Jul. 2016 / Accepted: 10 Aug. 2016 / Published: 8 Sep. 2016

Index Terms

Image Pre-processing, Convolutional Neural Network, Bengali Numeral, Handwritten Numeral Recognition


Recognition of handwritten numerals has gained much interest in recent years due to its various potential applications. Bengali is the fifth ranked among the spoken languages of the world. However, due to inherent difficulties of Bengali numeral recognition, a very few study on handwritten Bengali numeral recognition is found with respect to other major languages. The existing Bengali numeral recognition methods used distinct feature extraction techniques and various classification tools. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this paper, we have investigated a CNN based Bengali handwritten numeral recognition scheme. Since English numerals are frequently used with Bengali numerals, handwritten Bengali-English mixed numerals are also investigated in this study. The proposed scheme uses moderate pre-processing technique to generate patterns from images of handwritten numerals and then employs CNN to classify individual numerals. It does not employ any feature extraction method like other related works. The proposed method showed satisfactory recognition accuracy on the benchmark data set and outperformed other prominent existing methods for both Bengali and Bengali-English mixed cases. 

Cite This Paper

M. A. H. Akhand, Mahtab Ahmed, M. M. Hafizur Rahman,"Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.9, pp.40-50, 2016. DOI: 10.5815/ijigsp.2016.09.06


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