Sparsity-based super-resolution for offline handwriting recognition

Authors:
Shiv Vitaladevuni;Huaigu Cao;David Belanger;Krishna Subramanian;Rohit Prasad;Prem Natarajan
Affiliations:
Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA;Raytheon BBN Technologies, Cambridge, MA
Venue:
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Year:
2011

Citing 10
Cited 0

Improving resolution by image registration

CVGIP: Graphical Models and Image Processing
Example-Based Super-Resolution

IEEE Computer Graphics and Applications
Probabilistic Model for Segmentation Based Word Recognition with Lexicon

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Bayesian Super-Resolution of Text in Video with a Text-Specific Bimodal Prior

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Offline Arabic Handwriting Recognition: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-based single document image super-resolution: a global MAP approach with outlier rejection

Multidimensional Systems and Signal Processing
Improvements in BBN's HMM-Based Offline Arabic Handwriting Recognition System

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach

SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Shape-DNA: Effective Character Restoration and Enhancement for Arabic Text Documents

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a sparsity-based approach to super-resolution for handwritten document images, and demonstrate that it improves handwriting recognition accuracy. Given high resolution training images, low and high resolution dictionaries are constructed by extracting patches. The low resolution patches are adapted to expected distortions in the out-ofdomain test data using image filters. The intuition is that the low-resolution patches would match with artifacts in the test images and the pristine high-resolution patches would be back-projected to get a high-resolution version of test image. Patches from test images are projected onto the lowresolution dictionary under sparsity constraints. The projections coefficients are used to back-project high-resolution dictionary elements for super-resolution. Our experiments indicate that this super-resolution produces substantial improvements in handwriting recognition over bicubic. An important feature is the use of a separate, out-of-domain high resolution dataset for learning the dictionary and adapting it due to the unavailability of high resolution versions of the test data.