Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Object count/area graphs for the evaluation of object detection and segmentation algorithms
International Journal on Document Analysis and Recognition
A framework for the assessment of text extraction algorithms on complex colour images
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Scene Text Extraction with Edge Constraint and Text Collinearity
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
MAST: multi-script annotation toolkit for scenic text
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Multi-script and multi-oriented text localization from scene images
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
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
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A competition was organized by the authors to detect text from scene images. The motivation was to look for script-independent algorithms that detect the text and extract it from the scene images, which may be applied directly to an unknown script. The competition had four distinct tasks: (i) text localization and (ii) segmentation from scene images containing one or more of Kannada, Tamil, Hindi, Chinese and English words. (iii) English and (iv) Kannada word recognition task from scene word images. There were totally four submissions for the text localization and segmentation tasks. For the other two tasks, we have evaluated two algorithms, namely nonlinear enhancement and selection of plane and midline analysis and propagation of segmentation, already published by us. A complete picture on the position of an algorithm is discussed and suggestions are provided to improve the quality of the algorithms. Graphical depiction of f-score of individual images in the form of benchmark values is proposed to show the strength of an algorithm.