Affine-Invariant Recognition of Gray-Scale Characters Using Global Affine Transformation Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Extracting Textual Inserts from Digital Videos
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Automatic Thresholding of Gray-level Using Multi-stage Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Video text recognition using feature compensation as category-dependent feature extraction
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
The image text recognition graph (iTRG)
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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
An algorithm for colour-based natural scene text segmentation
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Recognizing natural scene characters by convolutional neural network and bimodal image enhancement
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
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This paper proposes a new technique of segmentation and recognition of characters with a wide variety of image degradations and complex backgrounds in natural scenes. The key ideas are twofold. One is segmentation of character and background by local/adaptive binarization of one of Cyan/Magenta/Yellow (CMY) color planes with the maximum breadth of histogram. The other is affineinvariant grayscale character recognition using global affine transformation (GAT) correlation. In experiments, we use a total of 698 test images extracted from the public ICDAR 2003 robust OCR dataset containing images of single characters in natural scenes. In advance, we classify those images into seven groups according to the degree of image degradations and/or background complexity. On the other hand, we prepare a single-font set of 62 alphanumerics for templates. Experimental results show an average recognition rate of 70.3%, ranging from 95.5% for clear images to 24.3% for littlecontrast images.