Digital Image Processing
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
Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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
An MRF Model for Binarization of Natural Scene Text
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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
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)
MAPS: midline analysis and propagation of segmentation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
| Hi-index | 0.00 |
We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.