title: A novel word spotting method based on recurrent neural networks.
abstract: 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.
authors.affiliation: Institute of Computer Science and Applied Mathematics (IAM), University of Bern, Neubrückstrasse 10, Bern CH-3012, Switzerland. frinken@iam.unibe.ch
authors.forename: Volkmar
authors.initials: V
authors.lastname: Frinken
authors.forename: Andreas
authors.initials: A
authors.lastname: Fischer
authors.forename: R
authors.initials: R
authors.lastname: Manmatha
authors.forename: Horst
authors.initials: H
authors.lastname: Bunke