Abstract
Searching handwritten documents is a relatively unexplored frontier for documents in any language. Traditional approaches use either image-based or text-based techniques. This paper describes a framework for versatile search where the query can be either text or image, and the retrieval method fuses text and image retrieval methods. A UNICODE and an image query are maintained throughout the search, with the results being combined by a neural network. Preliminary results show positive results that can be further improved by refining the component pieces of the framework (text transcription and image search).
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Srihari, S.N., Ball, G.R., Srinivasan, H. (2008). Versatile Search of Scanned Arabic Handwriting. In: Doermann, D., Jaeger, S. (eds) Arabic and Chinese Handwriting Recognition. SACH 2006. Lecture Notes in Computer Science, vol 4768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78199-8_4
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DOI: https://doi.org/10.1007/978-3-540-78199-8_4
Publisher Name: Springer, Berlin, Heidelberg
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