Document Image Decoding by Heuristic Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Decoding Using Markov Source Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training on Severely Degraded Text-Line Images
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Video text recognition using feature compensation as category-dependent feature extraction
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
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An approach to supervised training of character templates from page images and unaligned transcriptions is proposed. The template training problem is formulated as one of constrained maximum likelihood parameter estimation within the document image decoding framework. This leads to a three-phase iterative training algorithm consisting of transcription alignment, aligned template estimation (ATE), and channel estimation steps. The maximum likelihood ATE problem is shown to be NP-complete and, thus, an approximate solution approach is developed. An evaluation of the training procedure in a document-specific decoding task, using the University of Washington UW-II database of scanned technical journal articles, is described.