Scene Text Recognition using Higher Order Language Priors - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

Scene Text Recognition using Higher Order Language Priors

(1) , (2, 3) , (1)
1
2
3

Abstract

The problem of recognizing text in images taken in the wild has gained significant attention from the computer vision community in recent years. Contrary to recognition of printed documents, recognizing scene text is a challenging problem. We focus on the problem of recognizing text extracted from natural scene images and the web. Significant attempts have been made to address this problem in the recent past. However, many of these works benefit from the availability of strong context, which naturally limits their applicability. In this work we present a framework that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary. We show experimental results on publicly available datasets. Furthermore, we introduce a large challenging word dataset with five thousand words to evaluate various steps of our method exhaustively. The main contributions of this work are: (1) We present a framework, which incorporates higher order statistical language models to recognize words in an unconstrained manner (i.e. we overcome the need for restricted word lists, and instead use an English dictionary to compute the priors). (2) We achieve significant improvement (more than 20%) in word recognition accuracies without using a restricted word list. (3) We introduce a large word recognition dataset (atleast 5 times larger than other public datasets) with character level annotation and benchmark it.
Fichier principal
Vignette du fichier
mishra12a.pdf (272.95 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00818183 , version 1 (17-10-2013)

Identifiers

Cite

Anand Mishra, Karteek Alahari, C.V. Jawahar. Scene Text Recognition using Higher Order Language Priors. BMVC - British Machine Vision Conference, Sep 2012, Surrey, United Kingdom. ⟨10.5244/C.26.127⟩. ⟨hal-00818183⟩
1277 View
902 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More