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.

这篇论文介绍了一种算法，能够在 OCR 系统中在运行时高效生成和附加特定领域的基于单词的语言模型，并且介绍了一种改进的 CTC 束搜索解码器，有效地使得假设能够基于可能的未来词汇完成保持竞争，从而显著降低了识别专业领域材料时的词错误率。