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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 7 doi: 10.1631/FITEE.1600996

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

. Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India.. Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.. Synchromedia Laboratory, École de Technologie Supérieure, Montreal H3C 1K3, Canada

Available online: 2017-09-20

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Abstract

Unconstrained offline handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons (MLPs) in either a hybrid or a tandem fashion into hidden Markov models (HMMs). However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offline handwriting recognition. In the proposed model, deep belief networks are adopted to learn the compact representations of sequential data, while HMMs are applied for (sub-)word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari (an Indian script). Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs tandem approaches.

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