Document image decoding approach to character template estimation

An approach to supervised training of document-specific character templates from sample page images and unaligned transcriptions is presented. The template estimation problem is formulated as one of constrained maximum likelihood parameter estimation within the document image decoding (DID) framework. This leads to a two-phase iterative training algorithm consisting of transcription alignment and aligned template estimation (ATE) steps. The ATE step is the heart of the algorithm and involves assigning template pixel colors to maximize likelihood while satisfying a template disjointness constraint. In one large-scale experiment, use of document-specific templates resulted in a character error rate that was about an order of magnitude less than that of a commercial omni-font OCR program.