Confidence-scoring post-processing for off-line handwritten-character recognition verification

We apply confidence-scoring techniques to verify the output of an off-line handwritten-character recognizer. We evaluate a variety of scoring functions, including likelihood ratios and estimated posterior probabilities of correctness, in a post-processing mode, to generate confidence scores. Using the post-processor in conjunction with a neural-net-based recognizer, on mixed-case letters, receiver-operating-characteristic (ROC) curves reveal that our post-processor is able to reject correctly 90% of recognizer errors while only falsely rejecting 18.6% of correctly-recognized letters. For isolated-digit recognition, we achieve a correct rejection rate of 95% while keeping false rejection down to 8.7%.

[1]  Venu Govindaraju,et al.  The HOVER system for rapid holistic verification of off-line handwritten phrases , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[2]  Benoît Maison,et al.  Toward island-of-reliability-driven very-large-vocabulary on-line handwriting recognition using character confidence scoring , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  Jianchang Mao,et al.  Automated forms-processing software and services , 1996, IBM J. Res. Dev..

[4]  Mitch Weintraub,et al.  Explicit word error minimization in n-best list rescoring , 1997, EUROSPEECH.

[5]  M. Perrone.  Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization , 1993 .

[6]  B. Chigier,et al.  Rejection and keyword spotting algorithms for a directory assistance city name recognition application , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Patrick J. Grother,et al.  NIST Special Database 19 Handprinted Forms and Characters Database , 1995 .

[8]  Mitch Weintraub,et al.  Neural-network based measures of confidence for word recognition , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Jianchang Mao,et al.  A two-stage multi-network OCR system with a soft pre-classifier and a network selector , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[10]  Nikolai Gorski.  Optimizing error-reject trade off in recognition systems , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[11]  Proceedings Seventh International Conference on Document Analysis and Recognition , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[12]  Andreas Stolcke,et al.  Finding consensus among words: lattice-based word error minimization , 1999, EUROSPEECH.

[13]  Michael Perrone,et al.  Confidence modeling for verification post-processing for handwriting recognition , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.