Scene text detection using graph model built upon maximally stable extremal regions
Pattern Recognition Letters
Large-lexicon attribute-consistent text recognition in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Graph-Based detection of objects with regular regions
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Text extraction from scene images by character appearance and structure modeling
Computer Vision and Image Understanding
MAPS: midline analysis and propagation of segmentation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Benchmarking recognition results on camera captured word image data sets
Proceeding of the workshop on Document Analysis and Recognition
A text reading algorithm for natural images
Image and Vision Computing
Accurate and robust text detection: a step-in for text retrieval in natural scene images
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
Text extraction from natural scene image: A survey
Neurocomputing
Fast perspective recovery of text in natural scenes
Image and Vision Computing
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
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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.