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It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve...
The segmentation of touching characters is still a challenging problem in offline Chinese handwriting recognition. One feasible solution is through the over-segmentation strategy which maintains a high recall of correct cuts between adjacent characters and a moderate level of redundant cuts within a single character. Previous redundant cut filtering methods rely on either pure heuristics or learned...
Automatic processing of large volume scanned Chinese bank statements is a urgent demand recently. Conventional methods can not well handle the following challenges of this problem: various layout styles, noises, and especially requirement of fast speed for large Chinese character set. This paper proposes a knowledge based table recognition method to meet fast speed requirement with good accuracy....
In offline handwritten text recognition, the separation of touching characters remains a challenge due to the variability of touching structures. This paper proposes a new touching character separation method for Chinese handwriting based on skeleton analysis and contour analysis incorporating the visibility of separating points. Separating points are detected from strokes that are common in both...
The splitting of touching characters remains a challenge in over-segmentation, which is crucial to the performance of integrated segmentation-recognition of handwritten character strings. In this paper, we propose a new method based on contour analysis for touching character splitting in Chinese handwriting. To reliably locate splitting points on the contour of touching pattern, we pair upper and...
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