Bayesian Active Learning for Keyword Spotting in Handwritten Documents
Gaurav Kumar, Venu Govindaraju · 2014
We propose the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the keyword and background text in an active learning framework. It takes into account the local character level scores and global word level scores to distinguish keywords from non-keywords. We propose to apply the bayesian active learning strategy to identify the regions of sample space from which more meaningful labeled samples of keywords and non-keywords can be extracted. This work is an extension to our previous work which used a variational dynamic background model to model the large variations of background text. The approach has been tested on IAM dataset for English. The results show that a decent background model can be learned in a more quicker and efficient manner using the BALD framework. The approach outperforms our prior work and other state of the art approaches.