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A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words

Identifieur interne : 000F10 ( Main/Exploration ); précédent : 000F09; suivant : 000F11

A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words

Auteurs : Farshideh Einsele [Suisse] ; Rolf Ingold [Suisse] ; Jean Hennebert [Suisse]

Source :

RBID : ISTEX:8736746BFF748D608EA398BED3ED9DAA21A88C91

Abstract

Abstract: In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character.

Url:
DOI: 10.1007/978-3-540-77046-6_63


Affiliations:


Links toward previous steps (curation, corpus...)


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