Real-Time Lexicon-Free Scene Text Localization and Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00300740" target="_blank" >RIV/68407700:21230/16:00300740 - isvavai.cz</a>

Result on the web

<a href="http://cmp.felk.cvut.cz/~neumalu1/Neumann_TPAMI2015.pdf" target="_blank" >http://cmp.felk.cvut.cz/~neumalu1/Neumann_TPAMI2015.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2015.2496234" target="_blank" >10.1109/TPAMI.2015.2496234</a>

Result language

angličtina

Original language name

Real-Time Lexicon-Free Scene Text Localization and Recognition

Original language description

An end-to-end real-time text localization and recognition method is presented. Its real-time performance is achieved by posing the character detection and segmentation problem as an efficient sequential selection from the set of Extremal Regions. The ER detector is robust against blur, low contrast and illumination, color and texture variation. In the first stage, the probability of each ER being a character is estimated using features calculated by a novel algorithm in constant time and only ERs with locally maximal probability are selected for the second stage, where the classification accuracy is improved using computationally more expensive features. A highly efficient clustering algorithm then groups ERs into text lines and an OCR classifier trained on synthetic fonts is exploited to label character regions. The most probable character sequence is selected in the last stage when the context of each character is known. The method was evaluated on three public datasets. On the ICDAR 2013 dataset the method achieves state-of-the-art results in text localization; on the more challenging SVT dataset, the proposed method significantly outperforms the state-of-the-art methods and demonstrates that the proposed pipeline can incorporate additional prior knowledge about the detected text. The proposed method was exploited as the baseline in the ICDAR 2015 Robust Reading competition, where it compares favourably to the state-of-the art.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

—

Volume of the periodical

38

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1872-1885

UT code for WoS article

000381432700012

EID of the result in the Scopus database

2-s2.0-84981285560