OCR Language Models with Custom Vocabularies
Peter Garst Reeve Ingle Yasuhisa Fujii
Language models are useful adjuncts to optical models for producing accurate
optical character recognition (OCR) results. One factor which limits the power
of language models in this context is the existence of many specialized domains
with language statistics very different from those implied by a general
language model - think of checks, medical prescriptions, and many other
specialized document classes. This paper introduces an algorithm for
efficiently generating and attaching a domain specific word based language
model at run time to a general language model in an OCR system. In order to
best use this model the paper also introduces a modified CTC beam search
decoder which effectively allows hypotheses to remain in contention based on
possible future completion of vocabulary words. The result is a substantial
reduction in word error rate in recognizing material from specialized domains.