Keyword Spotting Framework Using Dynamic Background Model

Abstract

An important task in Keyword Spotting in handwritten documents is to separate Keywords from Non Keywords. Very often this is achieved by learning a filler or background model. A common method of building a background model is to allow all possible sequences or transitions of characters. However, due to large variation in handwriting styles, allowing all possible sequences of characters as background might result in an increased false reject. A weak background model could result in high false accept. We propose a novel way of learning the background model dynamically. The approach first used in word spotting in speech uses a feature vector of top K local scores per character and top N global scores of matching hypotheses. A two class classifier is learned on these features to classify between Keyword and Non Keyword.