Recognition of handprinted chinese characters via stroke relaxation

Cheng, Fang‐Hsuan; Hsu, Wen‐Hsing; Kuo, Ming-Chuan

doi:10.1016/0031-3203(93)90112-a

Cited by 36 publications

(9 citation statements)

References 8 publications

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“…Again, there are two types of image representation by graphs. The first explores relationships between strokes and edges (Lu et al 1991) and second, uses coordinates of the character shape (Cheng et al 1993). A number of techniques extract features from character's contours.…”

Section: Crossing and Distancesmentioning

confidence: 99%

Off-line cursive script recognition: current advances, comparisons and remaining problems

Rehman

Saba

2011

Artif Intell Rev

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This paper presents detailed review in the field of off-line cursive script recognition. Various methods are analyzed that have been proposed to realize the core of script recognition in a word recognition system. These methods are discussed in view of the two most important properties of such systems: size and nature of the lexicon involved and whether or not a segmentation stage is present. Script recognition techniques are classified into three categories: firstly, segmentation-free methods or holistic approaches, that compare a sequence of observations derived from whole word image with similar references of words in the small lexicon. Secondly, segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word similar to human-like reading technique, in which secure features found all over the word are used to boot-strap a few candidates for a final evaluation phase; thirdly, hybrid approaches. Additionally, different feature extraction techniques are elaborated in conjunction with the classification process. In this scenario, implications of single and multiple classifiers are also observed. Finally, remaining problems are highlighted along with possible suggestion and strategies to solve them.

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Section: Crossing and Distancesmentioning

confidence: 99%

Off-line cursive script recognition: current advances, comparisons and remaining problems

Rehman

Saba

2011

Artif Intell Rev

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“…There are two kinds of image representation by graphs. The first kind uses the coordinates of the character shape [33]. The second kind is an abstract representation with nodes corresponding to the strokes and edges corresponding to the relationships between the strokes [109].…”

Section: ) Global Transformation and Series Expansionmentioning

confidence: 99%

An overview of character recognition focused on off-line handwriting

Arıca

Yarman-Vural

2001

IEEE Trans. Syst., Man, Cybern. C

390

207

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Character recognition (CR) has been extensively studied in the last half century and progressed to a level sufficient to produce technology driven applications. Now, the rapidly growing computational power enables the implementation of the present CR methodologies and creates an increasing demand on many emerging application domains, which require more advanced methodologies.This material serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques with their superiorities and weaknesses are reviewed. Finally, the current status of CR is discussed, and directions for future research are suggested. Special attention is given to the off-line handwriting recognition since this area requires more research to reach the ultimate goal of machine simulation of human reading.

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“…Many researchers (Cheng, 1998;Cheng, Hsu, & Kuo, 1993;Chou & Tsai, 1991) proposed stroke relaxation matching methods for recognizing complicated Chinese characters rather than Kanji characters (Yamamoto & Rosenfeld, 1982). However, iterative stroke-matching algorithms are too much computationally intensive to be implemented.…”

Section: Other Related Workmentioning

confidence: 99%