Recognition algorithm based on wavelet transform for handprinted Chinese characters

Shioyama, Tadayoshi; Wu, Huaiyu; Nojima, T.

doi:10.1109/icpr.1998.711123

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…Holistic approaches ignore such components entirely, with the idea that they may be too hard to individually identify-much as recognizing individual characters in the middle of a word is very difficult in script based languages. They try to holistically recognize the character [10,[37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]. Holistic During recognition, a coarse classifier is often used to prune the search candidates of fine classifier.…”

Section: Recognitionmentioning

confidence: 99%

Offline Chinese handwriting recognition: an assessment of current technology

Srihari

Yang

Ball

2007

Front. Comput. Sc. China

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Offline Chinese handwriting recognition (OCHR) is a typically difficult pattern recognition problem. Many authors have presented various approaches to recognizing its different aspects. We present a survey and an assessment of relevant papers appearing in recent publications of relevant conferences and journals, including those appearing in ICDAR, SDIUT, IWFHR, ICPR, PAMI, PR, PRL, SPIE-DRR, and IJDAR. The methods are assessed in the sense that we document their technical approaches, strengths, and weaknesses, as well as the data sets on which they were reportedly tested and on which results were generated. We also identify a list of technology gaps with respect to Chinese handwriting recognition and identify technical approaches that show promise in these areas as well as identify the leading researchers for the applicable topics, discussing difficulties associated with any given approach.

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Section: Recognitionmentioning

confidence: 99%

Offline Chinese handwriting recognition: an assessment of current technology

Srihari

Yang

Ball

2007

Front. Comput. Sc. China

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“…Haar wavelet transform. Wavelet transformation (Shioyama et al, 1998;Huang and Huang, 2001) was used to reduce the dimension of the feature vector. Although there are many different wavelet families, such as Haar, Daubechies, Coiflet, Symmlet and Mallat, only the Haar wavelet transformation will be considered due to its simplicity.…”

Section: X-graph and Y-graphmentioning

confidence: 99%

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

Fah¹,

Lee²,

Rijal³

et al. 2010

International Journal of Applied Mathematics and Computer Science

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This paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). The X-graph and Y -graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using the X-graph and the Y-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R 2 p ) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.

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“…Wavelet-based approaches are becoming increasingly popular in pattern recognition, and have recently been applied to character recognition [4]. However, JKanji's wavelet decomposition is unrelated to prior research in this area; it was inspired by work in sketch-based image retrieval [2].…”

Section: Wavelet Decompositionmentioning

confidence: 99%

“…The images are filtered (both horizontally and vertically) using a simple variant of the Haar wavelet 4 . Although the Haar basis is rarely the best choice for filtering natural images, our experiments have shown that it outperforms Daubechies and cubic spline bases in the kanji completion domain.…”

Section: Wavelet Decompositionmentioning

confidence: 99%

“…We hypothesize that this is because the binary images created by the sketchpad application consist of crisply separated black and white regions (see Figure 1) whose sharp discontinuities are effectively expressed by the Haar basis. 4 Identical to the Haar wavelet except: (1) scaling constants are omitted; (2) averages are replaced by sums. This integer variant is more efficient than the standard Haar.…”

Section: Wavelet Decompositionmentioning

confidence: 99%

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