Stochastic Segment Modeling for Offline Handwriting Recognition

In this paper, we present a novel approach for incorporating structural information into the hidden Markov Modeling (HMM) framework for offline handwriting recognition. Traditionally, structural features have been used in recognition approaches that rely on accurate segmentation of words into smaller units (sub-words or characters). However, such segmentation based approaches do not perform well on real-world handwritten images, because breaks and merges in glyphs typically create new connected components that are not observed in the training data. To mitigate the problem of having to derive accurate segmentation from connected components, we present a novel framework where the HMM based recognition system trained on shorter-span features is used to generate the 2-D character images (the "Stochastic Segments"), and then another classifier that uses structural features extracted from the stochastic character segments generates a new set of scores. Finally, the scores from the...

• Type: Journal
• Year: 2009
• Where: ICDAR
• Authors: Premkumar Natarajan, Krishna Subramanian, Anurag Bhardwaj, Rohit Prasad
• Keywords: Accurate Segmentation, Document Analysis, ICDAR 2009, Structural, Structural Features