The Postprocessing of Optical Character Recognition Based on Statistical Noisy Channel and Language Model

The techniques of image processing have been used in optical character recognition (OCR) for a long time. The recognition method evolved from early "pattern recognition" to "feature extraction" recently. The recognition rate is raised from 70% to 90%. But the character by character recognition technique has its limitation. Using language models to assist the OCR system in improving recognition rate is the topic of many recent researches. Recently, the related research on Chinese nature language processing has improved rapidly. These improvement include the Chinese word segmentation, syntax analysis, semantic analysis, collocation analysis, statistical language models. In this paper, we will propose a new techniques for Chinese OCR postprocessing and postediting. We combine noisy channel model and the technique of natural language processing to implement an OCR postprocessing system. From the result of experiments, we found noisy channel model very effective for postprocessing. Under the approach, it is possible to recover the correct character, even when it is not in the candidate list produced by the OCR system.