Construction of language models for an handwritten mail reading system

Olivier Morillot; Laurence Likforman-Sulem; Emmanuèle Grosicki

doi:10.1117/12.911965

23 January 2012 Construction of language models for an handwritten mail reading system

Olivier Morillot, Laurence Likforman-Sulem, Emmanuèle Grosicki

Proceedings Volume 8297, Document Recognition and Retrieval XIX; 82970S (2012) https://doi.org/10.1117/12.911965
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is introduced. This language model is based on bigrams and built from training document transcriptions only. Different experiments with various vocabulary sizes and language models have been conducted. Word Error Rate and Perplexity values are compared to show the interest of specific language models, fit to handwritten mail recognition task.

Citation Download Citation

Olivier Morillot, Laurence Likforman-Sulem, and Emmanuèle Grosicki "Construction of language models for an handwritten mail reading system", Proc. SPIE 8297, Document Recognition and Retrieval XIX, 82970S (23 January 2012); https://doi.org/10.1117/12.911965

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available