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A System for Offline Cursive Handwritten Word - DSpace MIT - dspace mit

A System for Offline Cursive Handwritten Word - DSpace MIT - dspace mit 2025

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The document presents a thesis by Marlon Abayan on a system for offline cursive handwritten word recognition, submitted to MIT in 1997. It introduces a novel approach called Hidden Markov Model with Multiple Observation Sequences (HMMMOS) to tackle the challenges of recognizing cursive handwriting, which includes variations and overlaps between characters. The system employs a preprocessor for word extraction, neural networks for character classification, and a dynamic programming algorithm for word recognition. The thesis discusses various neural network architectures tested, including Multilayer Perceptron (MLP), Recurrent Networks, and Hierarchical Mixture of Experts (HME). It also explores methods for improving recognition rates through verification and correction using lexicon context. The work concludes with results demonstrating the effectiveness of the proposed methods and suggests future research directions.

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