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Ine Recognition of Unconstrained Handwritten

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The document presents a system for offline recognition of large vocabulary unconstrained handwritten texts using Hidden Markov Models (HMMs) and Statistical Language Models (SLMs). It highlights the challenges of recognizing unconstrained texts compared to single word recognition, emphasizing the integration of language modeling with handwriting recognition. The authors conducted experiments with varying lexicon sizes and demonstrated that SLMs significantly improve recognition accuracy, particularly in datasets with multiple writers. The paper concludes by discussing the implications of their findings for future research in handwritten text recognition.

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