I.J. Image, Graphics and Signal Processing, 2016, 9, 40-50 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2016.09.06 Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition M. A. H. Akhand, Mahtab Ahmed Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh Email: {akhand, mahtab}@cse.kuet.ac.bd M. M. Hafizur Rahman Dept. of Computer Science, KICT, International Islamic University Malaysia (IIUM) Selangor, Malaysia Email: [email protected] Abstract—Recognition of handwritten numerals has progress in Roman, Chinese, and Arabic script [3, 4]. On gained much interest in recent years due to its various the other hand, recognition of handwritten Bengali potential applications. Bengali is the fifth ranked among numeral is largely neglected although it is a major the spoken languages of the world. However, due to language in Indian subcontinent having fifth ranked in the inherent difficulties of Bengali numeral recognition, a world and is the main language of Bangladesh. Some very few study on handwritten Bengali numeral Bengali numerals have very similar shape, and even in recognition is found with respect to other major the printed form few differs very little than other languages. The existing Bengali numeral recognition numerals. Therefore, Bengali numeral recognition is a methods used distinct feature extraction techniques and challenging task. various classification tools. Recently, convolutional An interesting aspect of Bengali documents is that neural network (CNN) is found efficient for image English entries are commonly available in those. These classification with its distinct features. In this paper, we include the text books written in Bengali scripts often have investigated a CNN based Bengali handwritten have entries in English especially the numerals; numeral recognition scheme. Since English numerals are Bangladeshi currencies contain both Bengali and English frequently used with Bengali numerals, handwritten numerals to represent values; handwritten Bengali- Bengali-English mixed numerals are also investigated in English mixed numerals are frequently found in this study. The proposed scheme uses moderate pre- Bangladesh while writing postal code, bank cheque, age, processing technique to generate patterns from images of number plate, mobile number and other tabular form handwritten numerals and then employs CNN to classify documents. Moreover, often people casually enter one or individual numerals. It does not employ any feature more English numerals which results a mixed-script extraction method like other related works. The proposed situation. There is a similarity in writing style of several method showed satisfactory recognition accuracy on the Bengali numerals (e.g., ‗০ ‘, ‗২ ‘,‘৪ ‘ and ‗৭ ‘) with benchmark data set and outperformed other prominent English numerals (e.g., ‗0‘ ,‘2‘, ‗8‘ and ‗9‘) that makes existing methods for both Bengali and Bengali-English Bengali-English mixed numeral recognition more mixed cases. challenging. The reason behind the uses of this mixed case is that English is used in parallel with Bengali in Index Terms—Image Pre-processing, Convolutional official works and education systems. Therefore, Bengali- Neural Network, Bengali Numeral, Handwritten Numeral English mixed numeral recognition system is challenging Recognition. and important for practical applications. Although several remarkable works are available for Bengali and English handwritten numeral recognition separately, a very few I. INTRODUCTION works are available for mixed Bengali-English numeral recognition whose performance are not at satisfactory Recognition of handwritten numerals has gained much level. interest in recent years due to its various potential The rest of the paper is organized as follows. Section II applications. These include postal system automation, reviews several related works and explains motivation of passports & travel document analysis, automatic bank the present study. Section III explains proposed cheque processing, and even for number plate recognition scheme using convolutional neural network identification [1, 2]. Research on recognition of (CNN) which contains dataset preparation, pre- unconstrained handwritten numerals has made impressive processing and classification. Section IV presents Copyright © 2016 MECS I.J. Image, Graphics and Signal Processing, 2016, 9, 40-50 Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition 41 experimental results of the proposed method and Recently, Wen and He [13] proposed a kernel and comparison of performance with other related works. Bayesian Discriminant based method to recognize Finally, a brief conclusion of the work is given in Section handwritten Bengali numeral. Most recently, Nasir and V. Uddin [14] proposed a hybrid system for recognition of handwritten Bengali numeral for the automated postal system, which performed feature extraction using k- II. RELATED WORKS means clustering, Baye‘s theorem and maximum of a Posteriori, then the recognition is performed using SVM. A few notable works are available for Bengali On the other hand, according to best of our knowledge, handwritten numeral recognition with respect to other a very few studies are available for Bengali-English popular Indian subcontinent scripts such as Devanagari mixed handwritten numeral recognition. Mustafi et al. [15] [5-7]. Bashar et al. [8] investigated a digit recognition proposed a system which extract topological and system based on windowing and histogram techniques. structural features of the handwritten Bengali and English Windowing technique is used to extract uniform features numerals using water overflow from the reservoir. For from scanned image files and then histogram is produced recognition, a MLP is trained with the extracted feature from the generated features. Finally, recognition of the values using Back-propagation (BP) algorithm. They digit is performed on the basis of generated histogram. considered 16 classes, considering similarity between Pal et al. [3] introduced a new technique based on the Bengali numerals ‗ ‘, ‗ ‘, ‗ ‘, ‗ ‘ with English concept of water overflow from the reservoir for feature ০ ২ ৪ ৭ numerals ‗0‘, ‗2‘, ‗8‘, ‗9‘, respectively, in same class. extraction and then employed binary tree classifier for Vazda et al. [16] investigated mixed Bengali-English handwritten Bengali numeral recognition. Basu et al. [4] numeral recognition in Indian postal document used Dempster-Shafer (DS) technique to combine the automation. At first they extracted numeral images from classification decisions obtained from two MLP based the destination address block section of a postal classifiers for handwritten Bengali numeral recognition document. They normalized the images into 28×28 pixel using two different feature sets. Feature sets they and train a 16 class MLP using these pixel values. To investigated are called shadow feature and centroid improve recognition accuracy, other two MLPs with 10 feature. Khan et al. [9] employed an evolutionary classes are considered for Bengali and English numeral approach to train artificial neural network for Bengali recognition. In recognition stage, at first the six digit pin- handwritten numeral. At first, they used boundary code is checked with 16 class MLP. If majority of the extraction on a numeral image in a single window by numerals are recognized as Bengali then the entire pin- horizontal-vertical scanning and scaled the image into code is considered to be written in Bengali and fixed sized matrix. Then, Multi-Layer Perceptron (MLP) recognized through 10 classes Bengali classifier. are evolved for recognition. Similarly, 10 class English classifier is used if the Wen et al. [10] proposed a handwritten Bengali majority of the numerals are recognized as English by the numeral recognition system for automatic letter sorting 16-class classifier. Bhattacharya and Chaudhuri [12] also machine. They used Support Vector Machine (SVM) investigated handwritten Bengali-English mixed numeral classifier combined with extensive feature extractor using recognition along with Bengali. They tested Bengali- Principal Component Analysis (PCA) and kernel PCA English mixed numerals in two different MLPs: a 16 (KPCA). Das et al. [11] also used SVM for classification class MLP as like previous studies and 20 class MLP but used different techniques for feature selection. Seven considering 10 classes for each of Bengali and English. different sets of variable sized local regions are generated The objective of this study is to develop a recognition using different region selection strategies in their work. A scheme for handwritten Bengali and Bengali-English genetic algorithm (GA) based region sampling strategy mixed numerals which is capable of providing high has been employed to select an optimal subset of local recognition accuracy. Toward this goal, we pre-processed regions containing high discriminating information about the handwritten images in a moderate way to generate the pattern shapes from the above mentioned set. patterns and then CNN is employed for classification of Bhattacharya and Chaudhuri [12] presented a Bengali and Bengali-English mixed handwritten numerals. multistage cascaded recognition scheme using wavelet- Recently, CNN is found efficient for image classification based multi-resolution representations and MLP with