Word-Graph based Handwriting Key-word Spotting: Impact of Word-Graph Size on Performance

Autores UPV
Año
CONGRESO Word-Graph based Handwriting Key-word Spotting: Impact of Word-Graph Size on Performance

Abstract

Key-Word Spotting (KWS) in handwritten documents is approached here by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. Linguistic context significantly boost KWS performance with respect to methods which ignore word contexts and/or rely on image-matching with pre-segmented isolated words. On the other hand, WG-based KWS can be significantly faster than other KWS approaches which directly work on the original images where, in general, computational demands are exceedingly high. A large WG contains most of the relevant information of the original text (line) image needed for KWS but, if it is too large, the computational advantages over traditional, image-matchingbased KWS become diminished. Conversely, if it is too small, relevant information may be lost, leading to degraded KWS precision/recall performance. We study the tradeoff between WG size and KWS information-retrieval performance. Results show that small, computationally cheap WGs can be used without loosing the excellent KWS performance achieved with huge WGs.