2009 ANovelConnectionistSystemforUnc

• (Graves et al., 2009) ⇒ Alex Graves, Marcus Liwicki, Santiago Fernandez, Roman Bertolami, Horst Bunke, and Jurgen Schmidhuber. (2009). “A Novel Connectionist System for Unconstrained Handwriting Recognition.” In: IEEE Transactions on Pattern Analysis and Machine Intelligence Journal, 31(5). doi:10.1109/TPAMI.2008.137

Subject Headings: Memory Augmented Neural Network

Cited By

• http://scholar.google.com/scholar?q=%222009%22+A+Novel+Connectionist+System+for+Unconstrained+Handwriting+Recognition
• http://dl.acm.org/citation.cfm?id=1525650.1525782&preflayout=flat#citedby

Quotes

Author Keywords

• Handwriting Recognition; Online Handwriting; Offline Handwriting; Connectionist Temporal Classification; Bidirectional Long Short-Term Memory; Recurrent Neural Networks; Hidden Markov Model.

Abstract

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 ANovelConnectionistSystemforUnc	Horst Bunke Jürgen Schmidhuber Alex Graves Santiago Fernandez Marcus Liwicki Roman Bertolami			A Novel Connectionist System for Unconstrained Handwriting Recognition				10.1109/TPAMI.2008.137		2009