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Script-Independent Text Line Segmentation in Freestyle Handwritten Documents
August 2008 (vol. 30 no. 8)
pp. 1313-1329
Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

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Index Terms:
Document analysis, Handwriting analysis, Document and Text Processing
Citation:
Yi Li, Yefeng Zheng, David Doermann, Stefan Jaeger, Yi Li, "Script-Independent Text Line Segmentation in Freestyle Handwritten Documents," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1313-1329, Aug. 2008, doi:10.1109/TPAMI.2007.70792
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