An HMM-MLP hybrid model for cursive script recognition
type: Item
instance of: scholarly article
title: An HMM-MLP hybrid model for cursive script recognition
zbMATH Open document ID: 0991.68653
author: Jinho Kim
author: Kye Kyung Kim
author: Ching Y. Suen
published in: PAA. Pattern Analysis and Applications
publication date: 2002-07-25T00:00:00Z
review text: This paper presents an HMM (Hidden Markov Model)-MLP (Multi-Layer Perceptron) hybrid model for recognising cursive script words. We adopt an explicit segmentation-based word level architecture to implement an HMM classifier. An efficient state transition model and a parameter re-estimation scheme are introduced to use non-scaled and non-normalised symbol vectors without having to label primitive vectors. This approach brings well-formed discrete signals for the variable state duration of the HMM. We also introduce a new probability measure as well as conventional schemes to combine the proposed HMMs and a general MLP. The main contributions of this model are a novel design of the segmentation-based variable length HMMs, and an efficient method of combining two distinct classifiers. Experiments have been conducted using the legal word database of CENPARMI with encouraging results.
Mathematics Subject Classification ID: 68U99
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 1771514
describes a project that uses: CENPARMI
MaRDI profile type: Publication
full work available at URL: s100440070003
OpenAlex ID: W1973759055
DOI: 10.1007/S100440070003
description: scientific article; zbMATH DE number 1771514