Two-stage approach to keyword spotting in handwritten documents

Mehdi Haji; Mohammad R. Ameri; Tien D. Bui; Ching Y. Suen; Dominique Ponson

doi:10.1117/12.2042265

24 March 2014 Two-stage approach to keyword spotting in handwritten documents

Mehdi Haji, Mohammad R. Ameri, Tien D. Bui, Ching Y. Suen, Dominique Ponson

Author Affiliations +

Proceedings Volume 9021, Document Recognition and Retrieval XXI; 90210P (2014) https://doi.org/10.1117/12.2042265
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.

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Mehdi Haji, Mohammad R. Ameri, Tien D. Bui, Ching Y. Suen, and Dominique Ponson "Two-stage approach to keyword spotting in handwritten documents", Proc. SPIE 9021, Document Recognition and Retrieval XXI, 90210P (24 March 2014); https://doi.org/10.1117/12.2042265

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KEYWORDS

Binary data

Detection and tracking algorithms

Image segmentation

Machine learning

Feature extraction

Transform theory

Image classification

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