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Abstract

Font-independent spotting of user-defined keywords in a scanned image. Word identification is based on features of the entire word without the need for segmentation or OCR, and without the need to recognize non-keywords. Font-independent character models are created using hidden Markov models (HMMs) and arbitrary keyword models are built from the character HMM components. Word or text line bounding boxes are extracted from the image, a set of features based on the word shape, (and preferably also the word internal structure) within each bounding box is extracted, this set of features is applied to a network that includes one or more keyword HMMs, and a determination is made. The identification of word bounding boxes for potential keywords includes the steps of reducing the image (say by 2.times.) and subjecting the reduced image to vertical and horizontal morphological closing operations. The bounding boxes of connected components in the resulting image are then used to hypothesize word or text line bounding boxes, and the original bitmaps within the boxes are used to hypothesize words. In a particular embodiment, a range of structuring elements is used for the closing operations to accommodate the variation of inter- and intra-character spacing with font and font size.

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First Claim