Recognition of Bangla compound characters using structural decomposition

Highlights

• The proper recognition of compound characters is a difficult problem due to their complex shapes.

• In this paper, we propose a novel character recognition method for Bangla compound characters.

• Our strategy is to decompose the compound character into simpler shape components.

• Our technique is applicable to printed and handwritten characters.

• Experiment is done on printed and handwritten Bangla compound characters.

Abstract

In this paper we propose a novel character recognition method for Bangla compound characters. Accurate recognition of compound characters is a difficult problem due to their complex shapes. Our strategy is to decompose a compound character into skeletal segments. The compound character is then recognized by extracting the convex shape primitives and using a template matching scheme. The novelty of our approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components. Our technique is applicable to both printed and handwritten characters. The proposed method performs well for complex-shaped compound characters, which were confusing to the existing methods.

Introduction

Optical character recognition (OCR) is the process of automatic computer recognition of optically scanned and digitized character images. Several OCR systems are available commercially in the market [1], [2]. OCR is a necessary step for tasks like converting books, documents, and office records into electronic form [3] which allows the widely available text processing tools to be used for retrieval and dissemination of information [4]. The electronic text takes up less storage space compared to the image, can be edited, searched [5], [6], [9] and formatted for better display and printing. It can be machine translated [7] and converted to speech [8].

OCR systems are available for Roman or English script [10] and for a few Asian scripts, such as Chinese [11], [12], Japanese [13], Korean [14], [15], and Arabic [16], [17]. In the last two decades, several OCR works have been reported on different Indian scripts, such as Bangla [18], Devanagari [19], Tamil [20], Malayalam [21], Oriya [22], Telugu [23], Kannada [24], Gurmukhi [25], Gujarati [26]. These works mainly deal with recognizing basic characters. However, the main challenge in designing an OCR for Indian scripts is to handle compound (also known as ‘conjunct’) characters which are formed by combining two or more basic characters. The complex shapes of these characters make the problem more difficult.

In this paper, we address the problem of compound character recognition in Bangla which is the second-most popular language in India and among the top ten languages all over the world [27]. Bangla script is used to write Assamese and Bengali (also called ‘Bangla’) languages. There are a large number of (near about 250) compound characters in Bangla. However, some of these characters are obsolete now-a-days. Hence, in our work, we have considered the most familiar character set (about 165 in number [28]) used in the Bangla literature. Many of them are very complex in shape compared to the Devanagari compound characters [29], [30]. Prior work on Bangla OCR includes [18], [31], [32], [33], [34] for printed basic characters and [35], [36], [37], [38], [39], [40] for handwritten basic characters. But the evidences of work on recognizing Bangla compound characters, as observed in the literature [18], [36], [41], [42], [34], are few.

The research on Bangla compound character recognition can be categorized into two sets of methods, developed for printed and handwritten characters. Chaudhuri and Pal [18] have proposed a template matching approach for printed Bangla compound character recognition. In this method, text digitization, noise removal, skew detection, and correction are done as part of preprocessing. The text documents are segmented into lines, words, and characters using horizontal–vertical projection profile analysis and head line removal techniques. They have used eight stroke-based features for representing a character and a filled-circle feature for representing a dot.

Garain and Chaudhuri [41] have proposed a template matching technique for recognizing Bangla printed compound characters. Run number vectors are computed using horizontal and vertical scans organized with respect to the centroid of the pattern. The vector is normalized and abbreviated so as to make it invariant to scaling, insensitive to character style variation and more effective for complex shaped characters. Matching is performed within a group of compound characters.

Sural and Das [34] have used the concept of fuzzy sets for recognizing printed Bangla compound characters. Hough transform is used to extract lines and circles. Attributes such as length, position and orientation are used to define a number of fuzzy sets. A three stage multi-layer perceptron (MLP) classifier, trained with a number of linguistic set memberships derived from the intersections on the basic fuzzy sets, can recognize the characters by their similarities to the different fuzzy pattern classes.

Pal et al. [42] have proposed an off-line Bangla handwritten compound character recognition method using modified quadratic discriminative function (MQDF) classifier. The features used are mainly based on the directional information obtained from the arc tangent of the gradient.

Das et al. [36] have recognized Bangla handwritten basic and compound characters using two different classifiers: multi-layer perceptron (MLP) and support vector machine (SVM). Features used are based on shadow, longest run, and quad-tree. The MLP classifier is used for recognizing different groups of characters. A confusion matrix is prepared for the recognition results of the MLP classifier. Classes having a high degree of mutual misclassification are further handled using an SVM classifier, which gives a better accuracy.

Proper recognition of compound characters for Bangla script is a difficult problem because of the complex structural characteristics of these characters. We highlight some typical characteristics of Bangla compound characters which render the problem quite difficult and challenging.

• Certain compound characters are very similar in shape and are referred to as confusing characters. Fig. 1 shows a representative set of pairs of confusing characters.

• Few compound characters, such as

  (
  +
  +
  ),
  (
  +
  +
  ),
  (
  +
  +
  ), have very complex shapes. It is seen that when a compound character is formed by three basic characters its shape becomes very complex.

• The shapes for handwritten versions of certain compound characters are quite different from their printed styles.

To address the aforementioned challenges we propose a novel character recognition method for Bangla compound characters, using topological features, extracted by analyzing the structural convexities of the script aksharas. We handle the complex shape of a compound character by decomposing it into convex shape primitives. The topological characteristics of the character are represented in the form of the layout of the shape primitives. We recognize the compound character by matching the extracted topological feature with the standard feature templates that we formulate for the compound characters. A unique aspect of this work is the formulation of character decomposition rules for getting simpler shapes within the character skeleton.

The rest of this paper is organized as follows. Section 2 describes the module for detecting compound characters in a dataset containing both basic and compound characters. The decomposition of compound characters into shape components is explained in Section 3. The skeletal segments are decomposed into strokes and represented as shape primitives using the method given in Section 4. In Section 5 we discuss the formulation of topological features and the similarity measure for feature templates for recognizing compound characters. Experimental results and related discussion are reported in Section 6. The concluding notes are given in Section 7.