A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR

Authors:
Jun Du;Qiang Huo
Affiliations:
National Engineering Laboratory for Speech and Language Information Processing (NEL-SLIP), University of Science and Technology of China, No. 96, JinZhai Road, Hefei, Anhui, PR China;Microsoft Research Asia, 13/F, Building 2, No. 5 Danling Street, Haidian District, Beijing, PR China
Venue:
Pattern Recognition
Year:
2013

Citing 14
Cited 0

Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Degraded text recognition using visual and linguistic context

Degraded text recognition using visual and linguistic context
Confidence Guided Progressive Search and Fast Match Techniques for High Performance Chinese/English OCR

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Style consistency in pattern fields

Style consistency in pattern fields
Style Context with Second-Order Statistics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Style Consistent Classification of Isogenous Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
High performance Chinese OCR based on Gabor features, discriminative feature extraction and model training

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Designing Compact Recognizers of Handwritten Chinese Characters Using Multiple-Prototype Based Classifiers

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Snap and Translate Using Windows Phone

ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Style transfer matrix learning for writer adaptation

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Study of Minimum Classification Error (MCE) Linear Regression for Supervised Adaptation of MCE-Trained Continuous-Density Hidden Markov Models

IEEE Transactions on Audio, Speech, and Language Processing
Self-corrective character recognition system

IEEE Transactions on Information Theory
Whole-Book Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a new discriminative linear regression approach to adaptation of a discriminatively trained prototype-based classifier for Chinese OCR. A so-called sample separation margin based minimum classification error criterion is used in both classifier training and adaptation, while an Rprop algorithm is used for optimizing the objective function. Formulations for both model-space and feature-space adaptation are presented. The effectiveness of the proposed approach is confirmed by a series of experiments for adaptation of font styles and low-quality text, respectively.