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This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple
Being able to search for words or phrases in historic handwritten documents is of paramount importance when preserving cultural heritage. Storing scanned pages of written text can save the information from degradation, but it does not make the textual information readily available. Automatic keyword spotting systems
into a high quality mosaic image. Second, we relax the conditions in the Maximum Clique based keyword detection algorithm.The new algorithm can handle watermark string with variant spacing. Therefore, not only English keywords, but also Japanese/Chinese keywords can be processed. The Experimental results show the
The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which
Hausdorff distance (HD) and its modifications provides one of the best approaches for matching of binary images. This paper proposes a formalism generalizing almost all of these HD based methods. Numerical experiments for searching words in binary text images are carried out with old Bulgarian typewritten text, printed Bulgarian Chrestomathy from 1884 and Slavonic manuscript from 1574.
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