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Issue No. 02 - February (2012 vol. 34)
ISSN: 0162-8828
pp: 211-224
A. Fischer , Inst. of Comput. Sci. & Appl. Math. (IAM), Univ. of Bern, Bern, Switzerland
V. Frinken , Inst. of Comput. Sci. & Appl. Math. (IAM), Univ. of Bern, Bern, Switzerland
R. Manmatha , Dept. of Comput. Sci., Univ. of Massachusetts, Amherst, MA, USA
H. Bunke , Inst. of Comput. Sci. & Appl. Math. (IAM), Univ. of Bern, Bern, Switzerland
Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.
recurrent neural nets, document image processing, handwriting recognition, hidden Markov models, text line recognition, novel word spotting method, recurrent neural networks, keyword spotting, handwritten documents, handwriting recognition, CTC token passing algorithm, hidden Markov models, Hidden Markov models, Artificial neural networks, Feature extraction, Indexes, Handwriting recognition, Image segmentation, Neural networks, Documentation, BLSTM., Index TermsKeyword spotting, offline handwriting, document analysis, historical documents, neural network
A. Fischer, V. Frinken, R. Manmatha, H. Bunke, "A Novel Word Spotting Method Based on Recurrent Neural Networks", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 211-224, February 2012, doi:10.1109/TPAMI.2011.113
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