Identification of fork points on the skeletons of handwritten Chinese characters

Liu, K.; Huang, Yongliang; Suen, Ching Y.

doi:10.1109/34.799914

Cited by 63 publications

(24 citation statements)

References 15 publications

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“…In the first category, usually a skeletonization algorithm is applied to the original image, and the search for the original tracing path is performed by selecting, at each branch point, the most plausible direction on the basis of both local information and information relative to the previously selected path [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Writing Order Recovery from Off-Line Handwriting by Graph Traversal

Cordella

Stefano

Marcelli

et al. 2010

2010 20th International Conference on Pattern Recognition

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We present a method to recover the dynamic writing order from static images of handwriting. The static handwriting is initially represented by its skeleton, which is then converted into a graph, whose arcs correspond to the skeleton branches, and nodes to either end point or branch point of the skeleton. Criteria derived by handwriting generation are then applied to transform the graph in such a way that all its nodes, but the first and the last, have an even degree, so that it can be traversed from the first to the last by using the Fleury's algorithm. The experimental results show that combining criteria derived from handwriting generation models with graph traversal leads to reconstruct the original sequence produced by a writer even in case of complex handwriting, i.e handwriting with retracing, crossings and pen-up's.

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Section: Introductionmentioning

confidence: 99%

Writing Order Recovery from Off-Line Handwriting by Graph Traversal

Cordella

Stefano

Marcelli

et al. 2010

2010 20th International Conference on Pattern Recognition

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“…A vertex is composed of a single terminal pixel or a cluster of junction pixel(s). We identify the terminal pixels and the junction pixels by the method proposed in [14]. A sequence of connecting pixels between two vertices is referred to as a segment.…”

Section: Analysis On Skeleton Imagementioning

confidence: 99%

A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

Qiao

Nishiara

Yasuhara

2006

IEEE Trans. Pattern Anal. Mach. Intell.

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Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the Edge Continuity Relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4, and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions.In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0%.

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“…Hence, it is natural to use junctions in handwritten document analysis. Liu et al [5] have shown the efficiency of using fork points on the skeletons for Chinese character recognition. It has also been used to extract features for Arabic handwriting recognition (see the survey [6]).…”

Section: Introductionmentioning

confidence: 99%