Mending broken handwriting with a macrostructure analysis method to improve recognition
Pattern Recognition Letters
Degraded character image restoration using active contours: a first approach
Proceedings of the 2002 ACM symposium on Document engineering
Restoration of Archival Documents Using a Wavelet Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binarization of Low Quality Text Using a Markov Random Field Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Extraction and recognition of artificial text in multimedia documents
Pattern Analysis & Applications
Video text recognition using sequential Monte Carlo and error voting methods
Pattern Recognition Letters
Robustness of Shape Descriptors to Incomplete Contour Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Contour Extraction Using Hierarchical Shape Representation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Automatic accurate broken character restoration for patrimonial documents
International Journal on Document Analysis and Recognition
Symbolic representation of two-dimensional shapes
Pattern Recognition Letters
Robust Binarization for Video Text Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Automatic Video Text Localization and Recognition
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
A double-threshold image binarization method based on edge detector
Pattern Recognition
A Robust System to Detect and Localize Texts in Natural Scene Images
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Text Localization in Natural Scene Images Based on Conditional Random Field
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Document Image Binarisation Using Markov Field Model
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Character Recognition under Severe Perspective Distortion
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Automatic detection and recognition of Korean text in outdoor signboard images
Pattern Recognition Letters
Edge Based Binarization for Video Text Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Laplacian Approach to Multi-Oriented Text Detection in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Gradient Vector Flow-Based Method for Video Character Segmentation
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
A spatial-temporal approach for video caption detection and recognition
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
Character recognition in video is a challenging task because low resolution and complex background of video cause disconnections, loss of information, loss of shapes of the characters etc. In this paper, we introduce a novel ring radius transform (RRT) and the concept of medial pixels on characters with broken contours in the edge domain for reconstruction. For each pixel, the RRT assigns a value which is the distance to the nearest edge pixel. The medial pixels are those which have the maximum radius values in their neighborhood. We demonstrate the application of these concepts in the problem of character reconstruction to improve the character recognition rate in video images. With ring radius transform and medial pixels, our approach exploits the symmetry information between the inner and outer contours of a broken character to reconstruct the gaps. Experimental results and comparison with two existing methods show that the proposed method outperforms the existing methods in terms of measures such as relative error and character recognition rate.