Unsupervised writer style adaptation for handwritten word spotting
José A. Rodriguez, Florent Perronnin, Gemma Sanchez, Josep Llados
We propose a novel approach for writer adaptation in a word spotting task. The method exploits the fact that a semi-continuous hidden Markov model separates
the word model parameters into (i) a shared codebook of shapes and (ii) a set of word-specific parameters.Our main contribution is to derive writer-specific
word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best knowledge of the authors, this is the first work dealing with adaptation for word spotting.
ICPR 2008 (nineteenth conference of the International Association for Pattern Recognition (IAPR). Tampa Bay, Florida, USA, Dec 8-11, 2008