Authors: Cao, Huaigu
Manohar, Vasant
Natarajan, Prem
Prasad, Rohit
Subramanian, Krishna
Title: Subword-based Stochastic Segment Modeling for Offline Arabic Handwriting Recognition
Language (ISO): en
Abstract: In this paper, we describe several experiments in which we use a stochastic segment model (SSM) to improve offline handwriting recognition (OHR) performance. We use the SSM to re-rank (re-score) multiple decoder hypotheses. Then, a probabilistic multi-class SVM is trained to model stochastic segments obtained from force aligning transcriptions with the underlying image. We extract multiple features from the stochastic segments that are sensitive to larger context span to train the SVM. Our experiments show that using confidence scores from the trained SVM within the SSM framework can significantly improve OHR performance. We also show that OHR performance can be improved by using a combination of character-based and parts-of-Arabic-words (PAW)-based SSMs.
Subject Headings: Confidence scores
Hidden Markov Modeling
Optical Character Recognition
Stochastic Segment Modeling
Issue Date: 2011-01-12
Is part of: First International Workshop on Frontiers in Arabic Handwritng Recognition, 2010
Appears in Collections:2010 - First International Workshop on Frontiers in Arabic Handwriting Recognition

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