Keyword spotting in unconstrained handwritten Chinese documents using contextual word model
Image and Vision Computing
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This paper describes a new document retrieval method that is tolerant of OCR segmentation errors in document images. To overcome the segmentation and recognition errors that most OCR-based retrieval systems suffer from, the proposed method consists of two processing phases. First, the OCR engine first generates multiple character-segmentation and recognition hypotheses. Then the retrieval engine extracts keywords from the recognition hypotheses by using lexicon-driven dynamic programming (DP) matching. We have applied this method to both handwritten and printed document images and have demonstrated its effectiveness in reducing false drops and false alarms.