Handwriting documents denoising and indexing using hermite transform

Authors:
Stéphane Bres;Véronique Eglin;Carlos Rivero
Affiliations:
LIRIS, INSA de Lyon, Batiment Jules VERNE, VILLEURBANNE, France;LIRIS, INSA de Lyon, Batiment Jules VERNE, VILLEURBANNE, France;LIRIS, INSA de Lyon, Batiment Jules VERNE, VILLEURBANNE, France
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

Citing 6
Cited 1

Multichannel Texture Analysis Using Localized Spatial Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating Text and Background in Degraded Document Images " A Comparison of Global Threshholding Techniques for Multi-Stage Threshholding

IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Independent component analysis for document restoration

International Journal on Document Analysis and Recognition
Show-through cancellation in scans of duplex printed documents

IEEE Transactions on Image Processing
Comparison of texture features based on Gabor filters

IEEE Transactions on Image Processing

Rotation-invariant texture features from the steered Hermite transform

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new system for handwriting documents denoising and indexing. This work is based on the Hermite Transform, which is a polynomial transform and a good model of the human visual system (HVS). We use this transformation to decompose handwriting documents into local frequencies and using this decomposition, we analyze the visual aspect of handwritings to compute similarity measures. A direct application is the management of document databases, allowing to find documents coming from the same author or to classify documents containing handwritings that have similar visual aspect. Moreover, ancient documents can contain degradations from different origins. It is often necessary to clean the backgrounds of those degraded documents before analysing them. The current results are very promising and show that it is possible to characterize handwritten drawings without any a priori graphemes segmentation.