TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Segmentation of Characters in Web Images Based on Human Colour Perception
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Gabor Filter Based Block Energy Analysis for Text Extraction from Digital Document Images
DIAL '04 Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04)
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A new wavelet-median-moment based method for multi-oriented video text detection
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
MAST: multi-script annotation toolkit for scenic text
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
MAST: multi-script annotation toolkit for scenic text
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Benchmarking recognition results on camera captured word image data sets
Proceeding of the workshop on Document Analysis and Recognition
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
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This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.