Abstract
Large quantities of scanned handwritten and printed documents are rapidly being made available for use by information storage and retrieval systems, such as for use by libraries. We present the design and performance of a language independent system for spotting handwritten/printed words in scanned document images. The technique is evaluated with three scripts: Devanagari (Sanskrit/Hindi), Arabic (Arabic/Urdu) and Latin (English). Three main components of the system are a word segmenter, a shape based matcher for words, and a search interface. The user gives a query which can be (i) A word image (to spot similar words from a collection of documents written in that script) or (ii) text (to look for the equivalent word images in the script). The candidate words that are searched in the documents are retrieved and ranked, where the ranking criterion is a similarity score between the query and the candidate words based on global word shape features. For handwritten English, a precision of 60% was obtained at a recall of 50%. An alternate approach comprising of prototype selection and word matching, that yields a better performance for handwritten documents is also discussed. For printed Sanskrit documents, a precision as high as 90% was obtained at a recall of 50%.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kuo, S., Agazzi, O.: Keyword spotting in poorly printed documents using 2-d hidden markov models. IEEE Trans. Pattern Analysis and Machine Intelligence 16, 842–848 (1994)
Burl, M., Perona, P.: Using hierarchical shape models to spot keywords in cursive handwriting. In: IEEE-CS Conference on Computer Vision and Pattern Recognition, June 23-28, pp. 535–540 (1998)
Kolz, A., Alspector, J., Augusteijn, M., Carlson, R., Popescu, G.V.: A line-oriented approach to word spotting in handwritten documents. Pattern Analysis and Applications 2(3), 153–168 (2000)
Manmatha, R., Rath, T.M.: Indexing of handwritten historical documents-recent progress. In: Symposium on Document Image Understanding Technology (SDIUT), pp. 77–85 (2003)
Zhang, B., Srihari, S.N., Huang, C.: Word image retrieval using binary features. In: Document Recognition and Retrieval XI, SPIE, San Jose, CA (2004)
Srihari, S.N., Srinivasan, H., Babu, P., Bhole, C.: Handwritten arabic word spotting using the cedarabic document analysis system. In: Proc. Symposium on Document Image Understanding Technology (SDIUT 2005), pp. 123–132 (2005)
Srihari, S.N., Cha, S.-H., Arora, H., Lee, S.: Individuality of handwriting. Journal of Forensic Sciences 47(4), 856–872 (2002)
Srihari, S.N., Zhang, B., Tomai, C., Lee, S., Shi, Z., Shin, Y.C.: A system for handwriting matching and recognition. In: Proceedings of the Symposium on Document Image Understanding Technology (SDIUT 2003), Greenbelt, MD (2003)
Srihari, S.N., Srinivasan, H., Babu, P., Bhole, C.: Spotting words in handwritten Arabic documents. In: Document Recognition and Retrieval XIII: Proceedings SPIE, San Jose, CA, pp. 606702–1–606702–12(2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Srihari, S.N., Ball, G.R. (2008). Language Independent Word Spotting in Scanned Documents. In: Buchanan, G., Masoodian, M., Cunningham, S.J. (eds) Digital Libraries: Universal and Ubiquitous Access to Information. ICADL 2008. Lecture Notes in Computer Science, vol 5362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89533-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-89533-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89532-9
Online ISBN: 978-3-540-89533-6
eBook Packages: Computer ScienceComputer Science (R0)