Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features

Authors:
A. Vinciarelli;S. Bengio
Affiliations:
-;-
Venue:
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Year:
2002

Citing 0
Cited 3

Discriminative Training for HMM-Based Offline Handwritten Character Recognition

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is based on a continuous density Hiddden Markov Model trained using either the raw data, or data transformed using Principal Component Analysis or Independent Component Analysis. Both techniques significantly improved the recognition rate of the system.Preprocessing, normalization and feature extraction are described as well as the training technique adopted. Several experiments were performed using a publicly available database. The accuracy obtained is the highest presented in the literature over the same data.