HMM-based Word Spotting in Handwritten Documents Using Subword Models

Handwritten word spotting aims at making document images amenable to browsing and searching by keyword retrieval. In this paper, we present a word spotting system based on Hidden Markov Models (HMM) that uses trained subword models to spot keywords. With the proposed method, arbitrary keywords can be spotted that do not need to be present in the training set. Also, no text line segmentation is required. On the modern IAM off-line database and the historical George Washington database we show that the proposed system outperforms a standard template matching approach based on dynamic time warping (DTW).