Cao, HuaiguManohar, VasantNatarajan, PremPrasad, RohitSubramanian, Krishna2011-01-122011-01-122011-01-12http://hdl.handle.net/2003/2756410.17877/DE290R-14709In 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.enConfidence scoresHidden Markov ModelingOptical Character RecognitionStochastic Segment Modeling004conference contribution