HMM OCR Node Pruning for Decoding Speed

The patent segments the set of all possible characters into two groups: a limited set of candidate characters that are likely to appear at the current position (based on language models and contextual information), and the remaining characters that are pruned from consideration. This segmentation allows the system to evaluate only the relevant subset of characters at each boundary, maintaining accuracy for likely candidates while dramatically reducing decoding time by eliminating evaluation of improbable characters.

The patent applies local quality by making the character evaluation process adaptive to each specific position in the text. Instead of uniformly evaluating all characters at every position (which would be computationally expensive for large character sets), the system uses language models and contextual information to assign different levels of scrutiny to different character positions. High-probability positions receive full evaluation while low-probability positions undergo aggressive pruning, optimizing the balance between OCR capability and computational complexity.