ICDAR 2011 Robust Reading Competition - Challenge 1: Reading Text in Born-Digital Images (Web and Email)

Karatzas, Dìmosthenis; Mestre, Sergi Robles; Mas, Jordi; Nourbakhsh, Farshad; Roy, Partha Pratim

doi:10.1109/icdar.2011.295

Cited by 89 publications

(49 citation statements)

References 10 publications

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“…The first challenge deals with born-digital images (web, email) whereas the second challenge deals with scene text recognition. A summary of the results of Challenge 1 is presented in [14]. This paper summarizes results of Challenge 2 on scene text recognition.…”

Section: Introductionmentioning

confidence: 99%

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Shahab

Shafait

Dengel

2011

2011 International Conference on Document Analysis and Recognition

352

202

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Abstract-Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance.

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Section: Introductionmentioning

confidence: 99%

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Shahab

Shafait

Dengel

2011

2011 International Conference on Document Analysis and Recognition

352

202

View full text Add to dashboard Cite

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“…We used (Wolf and Jolion, 2006) to evaluate our results following the approach of the ICDAR competition 2011 (Karatzas et al, 2011). We used the area precision thresholds proposed in the original publication: t p = 0.4 and we decreased the area recall threshold from t r = 0.8 to t r = 0.6 in order to be more flexible with accent and punctuation miss detections (e.g.À,È,É, ..., !, ?)…”

Section: Discussionmentioning

confidence: 99%

Automatic Text Localisation in Scanned Comic Books

Rigaud¹,

Karatzas²,

Weijer³

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comic book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.

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“…Since the last decade, the International Conference on Document Analysis and Recognition (ICDAR) has been developing datasets and running competitions under the title Robust Reading Competitions or Challenges. Since 2003, ICDAR 2003 was the frst publicly available dataset for text detection and recognition, two different competitions were run in ICDAR 2005 [11], one in ICDAR 2011 [12] and [13], one in ICDAR2013 [14] and fnally in ICDAR 2015 [15].…”

Section: Previous Workmentioning

confidence: 99%