Offline handwritten Chinese character recognition by radical decomposition

Abstract

Offline handwritten Chinese character recognition is a very hard pattern-recognition problem of considerable practical importance. Two popular approaches are to extract features holistically from the character image or to decompose characters structurally into component parts-usually strokes. Here we take a novel approach, that of decomposing into radicals on the basis of image information (i.e., without first decomposing into strokes). During training, 60 examples of each radical were represented by "landmark" points, labeled semiautomatically, with radicals in different characteristic positions treated as distinctly different radicals. Kernel principal-component analysis then captured the main (nonlinear) variations around the mean radical. During the recognition, the dynamic tunneling algorithm was used to search for optimal shape parameters in terms of chamfer distance minimization. Considering character composition as a Markov process in which up to four radicals are combined in some assumed sequential order, we can recognize complete, hierarchically-composed characters by using the Viterbi algorithm. This gave a character recognition rate of 93.5% characters correct (writer-independent) on a test set of 430,800 characters from 2,154 character classes composed of 200 radical categories, which is comparable to the best reported results in the literature. Although the initial semiautomatic landmark labeling is time consuming, the decomposition approach is theoretically well-motivated and allows the different sources of variability in Chinese handwriting to be handled separately and by the most appropriate meanseither learned from example data or incorporated as prior knowledge. Hence, high generalizability is obtained from small amounts of training data, and only simple prior knowledge needs to be incorporated, thus promising robust recognition performance. As such, there is very considerable potential for further development and improvement in the direction of larger character sets and less constrained writing conditions.

An optical music recognition system for traditional Chinese Kunqu Opera scores written in Gong-Che Notation

“…Optical character recognition (OCR) is an active field, particularly for handwritten documents in such languages as Roman [45], Arabic [46], Chinese [47], and Indian [48]. Several Chinese character recognition methods have been proposed, with the best known being the transformation invariant matching algorithm [49], adaptive confidence transform based classifier combination [50], probabilistic neural networks [51], radical decomposition [52], statistical character structure modeling [53], Markov random fields [54], and affine sparse matrix factorization [55].…”

Offline Chinese handwriting recognition: an assessment of current technology

“…The radical-based approach attempts to decompose the character into radicals and categorizes the character based on the component parts and their placement [1,[19][20][21][22][23][24]. Instead of attempting to recognize the radicals directly, stroke-based approaches try to break the character into component parts as strokes [2,[25][26][27][28][29][30][31][32][33][34][35][36], then recognize the character according to the stroke number, order, and position.…”

“…Daming Shi et al [1,[19][20][21][22][23] have done successful work on radical-based recognition for off-line handwritten Chinese characters. They published a series of papers that elaborated an approach based on nonlinear active shape modeling.…”

Stroke Based Handwritten Character Recognition

“…Arabic handwritten recognition [8] has a 89,3% of accuracy, and an optical character recognition systems for handwritten Gujarati numbers [12] have achieved approximately 82% of hit rate. However, [3] handwritten Chinese Character Recognition has a recognition rate between 96.4% and 96.6%. Of course, all of them use neural networks.…”