A method for text localization and recognition in real-world images

Lukas Neumann, Jiri Matas

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    348 Citations (Scopus)

    Abstract

    A general method for text localization and recognition in real-world images is presented. The proposed method is novel, as it (i) departs from a strict feed-forward pipeline and replaces it by a hypotheses-verification framework simultaneously processing multiple text line hypotheses, (ii) uses synthetic fonts to train the algorithm eliminating the need for time-consuming acquisition and labeling of real-world training data and (iii) exploits Maximally Stable Extremal Regions (MSERs) which provides robustness to geometric and illumination conditions. The performance of the method is evaluated on two standard datasets. On the Char74k dataset, a recognition rate of 72% is achieved, 18% higher than the state-of-the-art. The paper is first to report both text detection and recognition results on the standard and rather challenging ICDAR 2003 dataset. The text localization works for number of alphabets and the method is easily adapted to recognition of other scripts, e.g. cyrillics.

    Original languageEnglish
    Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
    Pages770-783
    Number of pages14
    Volume6494 LNCS
    EditionPART 3
    DOIs
    Publication statusPublished - 2011
    Publication typeA4 Article in conference proceedings
    Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
    Duration: 8 Nov 201012 Nov 2010

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 3
    Volume6494 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Conference

    Conference10th Asian Conference on Computer Vision, ACCV 2010
    Country/TerritoryNew Zealand
    CityQueenstown
    Period8/11/1012/11/10

    ASJC Scopus subject areas

    • General Computer Science
    • Theoretical Computer Science

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