Abstract: 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 disjointedness constraint. The training algorithm is demonstrated on a variety of English documents, including newspaper columns, 15th century books, degraded images of 19th century newspapers, and connected text in a script-like font. Three applications enabled by the training procedure are described - high accuracy document-specific decoding, transcription error visualization and printer font generation. !14
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