Conference Paper

A novel approach to recover writing order from single stroke offline handwritten images

Univ. of Electro-Commun., Tokyo, Japan
DOI: 10.1109/ICDAR.2005.25 Conference: Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on
Source: DBLP


Problem of recovering the writing order from single-stroked handwritten image can be seen as finding the smoothest Euler path in its graph representation. In this paper, a novel approach is proposed to solve the recovery problem within the framework of the edge contiguous relation (ECR). Firstly, we make local analyses to obtain the possible ECRs at each of the nodes; secondly a global trace is executed to find all of the candidate Euler paths and the smoothest one is selected as a final result. Based on two simple assumptions, we prove a series of theorems to obtain possible ECRs at even node. Double-traced lines are identified by using the weighted matching of general graph. Experiments on the scanned images and offline images converted from the online data of Unipen database have shown that our method achieved 95.2% correct recovery rate.

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Available from: Yu Qiao, Jul 31, 2015
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    • "Loop investigation has been considered in the context of enhancing offline handwritten word representation and the reconstruction of the genuine ordered list of strokes. It has been done mostly by using temporal (dynamic) information recovery techniques such as contour analysis [24], [25], gray-scale examination [26], [27], and path minimization [28], [29], [30]. Other methods include thinning/skeletonization [31], [32], [33], [34], [35], [36], [37], [38], [39] and morphological loop investigation [40], [41], [42]. "
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    ABSTRACT: Resolution of different types of loops in handwritten script presents a difficult task and is an important step in many classic word recognition systems, writer modeling, and signature verification. When processing a handwritten script, a great deal of ambiguity occurs when strokes overlap, merge, or intersect. This paper presents a novel loop modeling and contour-based handwriting analysis that improves loop investigation. We show excellent results on various loop resolution scenarios, including axial loop understanding and collapsed loop recovery. We demonstrate our approach for loop investigation on several realistic data sets of static binary images and compare with the ground truth of the genuine online signal.
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    • "The robust decision should be made in more global fashion. We achieve this by using maximum weighted matching algorithm [14], the details of which were described in our former work [10]. "
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    ABSTRACT: This paper addresses the problem to recover drawing order from single-stroked offline handwritten image. The recovery problem is formulated as to find the smoothest path to cover all the edges in the graph representation of an input image. The two main contributions are: (1) we introduce direction context to calculate the smoothness between edges; (2) the smoothest path is found by solving a new graph problem: the optimal Euler path problem. An O(n+m+nlog(m/2)) time algorithm is developed to find the optimal Euler path in a graph with 4-degree nodes only. The double traced lines are identified using the maximum weighted matching of general graph. Experimental results on about 13,000 static images converted from the online data in the Unipen database demonstrated the utility of our methods
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    ABSTRACT: In order to use handwritings as a universal man-machine interface, we assume a pen device — data-embedding pen — which can embed digital data into a handwriting by invisible ink in a real-time man- ner. This paper discusses the system design, application, and required technologies around the data-embedding pen. Especially, a novel stroke recovery algorithm is proposed for retrieving the embedded data along writing order. In the algorithm, embedded data is used to help the recovery. A simulation experiment showed that the algorithm can attain high accuracy on the stroke recovery and the data retrieval.
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