Identification of blur parameters from motion blurred images
Graphical Models and Image Processing
Text enhancement in digital video using multiple frame integration
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Digital Image Restoration
Modeling motion blur in computer-generated images
SIGGRAPH '83 Proceedings of the 10th annual conference on Computer graphics and interactive techniques
Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low resolution character recognition by dual eigenspace and synthetic degraded patterns
Proceedings of the 1st ACM workshop on Hardcopy document processing
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
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Camera-based character recognition has gained attention with the growing use of camera-equipped portable devices. One of the most challenging problems in recognizing characters with hand-held cameras is that captured images undergo motion blur due to the vibration of the hand. Since it is difficult to remove the motion blur from small characters via image restoration, we propose a recognition method without de-blurring. The proposed method includes a generative learning method in the training step to simulate blurred images by controlling blur parameters. The method consists of two steps. The first step recognizes the blurred characters based on the subspace method, and the second one reclassifies structurally similar characters using blur parameters estimated from the camera motion. We have experimentally proved that the effective use of motion blur improves the recognition accuracy of camera-captured characters.