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Whole-book recognition is a document image analysis strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy. We describe an algorithm which expects to be initialized with approximate iconic and linguistic models—derived from (generally errorful) OCR results and (generally imperfect) dictionaries—and then, guided entirely...
Can a high-performance document image recognition system be built without detailed knowledge of the application? Having benefited from the statistical machine learning revolution of the last twenty years, our architectures rely less on hand-crafted special-case rules and more on models trained on labeled-sample data sets. But urgent questions remain. When we can't collect (and label) enough real training...
Whole-book recognition is a document image analysis strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy. Our algorithm expects to be given approximate iconic and linguistic models-derived from (generally errorful) OCR results and (generally incomplete) dictionaries-and then, guided entirely by evidence internal to the test set, corrects...
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