Confidence-Scoring Post-Processing for Off-Line Handwritten-Character Recognition Verification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Protection through multimedia CAPTCHAs
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Protection through Intelligent and Multimedia Captchas
International Journal of Adaptive, Resilient and Autonomic Systems
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We explore a novel approach for handwriting recognition tasks whose intrinsic vocabularies are too large to be applied directly as constraints during recognition. Our approach makes use of vocabulary constraints, and addresses the issue that some parts of words may be written more recognizably than others. An initial pass is made with an HMM recognizer, without vocabulary constraints, generating a lattice of character-hypothesis arcs representing likely segmentations of the handwriting signal. Arc confidence scores are computed using a posteriori probabilities. The most confidently recognized characters are used to filter the overall vocabulary, generating a word subset manageable for constraining a second recognition pass. With a vocabulary of 273000 words, we can limit to 50000 words in the second pass and eliminate 39.3% of the word errors made by a one-pass recognizer without vocabulary constraints, and 18.3% of errors made using a fixed 30000-word set.