Online Unconstrained Handwritten Thai Character Recognition Using Multiple Representations

Abstract

This paper proposes an unconstrained handwritten counter clockwise direction as the main feature. Even Thai character recognition method using multiple representations.though this feature quite coincides with the way writers learnThe proposed method can recognize different handwritten Thai to write (Fig. 1), it may consist of noisy curves. Usually, the characters having small curve segments with clockwise and noise curve segment can be distinguished from the real curve counter clockwise directions, although the conventional method by the high amount of curvature of the real signal. But when such as the elastic matching method was difficult to recognize the written speed is fast, the curvature is reducing close to the them. As the experimental results, the recognition rate was 97.50% th e shown in fig. the curv is hard to and the recognition time (average) using the PC with 1.4 GHz noisy curve as shown in Fig. 1. These noisy curves are hard to Pentium 4 was 0.01 second. distinguish with normal noise reduction technique such as Fourier expansion or Fourier descriptor due to their similarity I. INTRODUCTIONin frequency and amplitude. In order to successfully use the curvature sequence in the recognition, one may have to Many methods for online character recognition have been identify real curvature from the noisy one.proposed [1]. However a few methods for online Thai character have been proposed [7,8,9]. In this paper, a new method for unconstrained handwritten Thai character recognition is proposed. The example of unconstraint handwritten character is shown in Fig. 1, where characters are written out of proportion and varied in amount of rotation Printed Handwritten (slant) and writing style. Most zone features [1] are less effecti * d . t stheo Mo pro onse some characters Fig. 1. Examples of unconstrained handwritten Thai character with skew effective clue to the out of proportion since some characters s and in proportion.will have big, small or omitted head. The proposed method will be compared with elastic matching which provides a very Dominance curvature good recognition rate and has been widely used since the early [2] and recently handwritten recognition research [3,4].The elastic matching can be used to perform matching the unknown writing with the prototype character and chosen the Distance A is smaller than distance B closest match as the recognized character. The elastic matching find the optimal alignment distance among all the n Fig. 2. The elastic matching of overall shape and missed dominant points of both matched characters by summation distance of curvature. all the matched point along the Viterbi path. This require 0(n2) computational cost proportional to the Viterbi matrix Test Prototype Test Prototype Test Prototype Test Prototype size [5]. Even though the Viterbi search reduce the ] 74 computational cost from exponential to O(n2), the cost still 0 = l | high to perform real time recognition, especially for a lot of i V i1 f J |j |m prototype characters. Another disadvantage of elastic matching...

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