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In order to capture the content of an imaged document but avoid the time-consuming full-scale OCR which is fragile to handle touching characters, a fast and segmentation- free keyword spotting method is proposed in this paper. The keyword spotting method is based on word shape coding technique. The proposed coding
A hidden-Markov-model (HMM)-based system for font-independent spotting of user-specified keywords in a scanned image is described. Word bounding boxes of potential keywords are extracted from the image using a morphology-based preprocessor. Feature vectors based on the external shape and internal structure of the word
Finder system. We show that improved text extraction results in the retrieval of a larger number of relevant images for a set of domain-relevant keyword searches.
Hausdorff distance (HD) and its modifications provides one of the best approaches for matching of binary images. This paper proposes a formalism generalizing almost all of these HD based methods. Numerical experiments for searching words in binary text images are carried out with old Bulgarian typewritten text, printed Bulgarian Chrestomathy from 1884 and Slavonic manuscript from 1574.
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