A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Color Text Extraction from Camera-based Images the Impact of the Choice of the Clustering Distance
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Color text extraction with selective metric-based clustering
Computer Vision and Image Understanding
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
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
An MRF Model for Binarization of Natural Scene Text
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
An algorithm for colour-based natural scene text segmentation
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Top-down and bottom-up cues for scene text recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
End-to-end scene text recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Recognition of Kannada characters extracted from scene images
Proceeding of the workshop on Document Analysis and Recognition
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
| Hi-index | 0.00 |
Scenic word images undergo degradations due to motion blur, uneven illumination, shadows and defocussing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scenic word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a sub-image and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation. This midline based approach limits the impact of degradations that happens to the image. The segmented text image is recognized using the trial version of Omnipage OCR. We have tested our method on ICDAR 2003 and ICDAR 2011 datasets. Our word recognition results of 64.5% and 71.6% are better than those of methods in the literature and also methods that competed in the Robust reading competition. Our method makes an implicit assumption that degradation is not present in the middle row.