Topic
Intelligent word recognition
About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.
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Patent•
28 Dec 1995
TL;DR: In this article, an adaptive weighting handwriting recognition device and method compares information representing handwritten input with stored recognition data at least some of which has a weighting value associated therewith, which can be further processed with user editing instructions to modify and correct the candidate recognition information.
Abstract: An adaptive weighting handwriting recognition device and method compares information representing handwritten input with stored recognition data at least some of which has a weighting value associated therewith. The weighting values remains fixed during comparison of the handwritten input and stored recognition data and provide candidate recognition information, which can be further processed with user editing instructions to modify and correct the candidate recognition information. During user editing, the weighting values of the stored recognition data associated with the corrected candidate recognition information modified are varied to enhance the likelihood of correct future handwriting recognition.
12 citations
25 Aug 2013
TL;DR: A novel binarization method that is especially effective on historical documents with the following characteristics, which includes free-form cursive handwritten text with significant but consistent slant and a CRF-based framework which handles bleeds using a novel approach.
Abstract: We present a novel binarization method that is especially effective on historical documents with the following characteristics: (a) the documents contain free-form cursive handwritten text with significant but consistent slant, (b) scanning artifacts resulting in the text and background pixels not having uniform intensity even within the same page, and (c) pages containing significant amount of bleeds from the other side of the page. In order to tackle the problem of non-uniform text and background intensity, we use a thresholding algorithm that works equally well for regions of the page containing text and regions of the page containing no text. We then combine this algorithm with a CRF-based framework which handles bleeds using a novel approach to further improve the quality of binarization. We compare the proposed binarization algorithm against other popular binarization algorithms both qualitatively using examples and quantitatively using the word error rate (WER) metric from performing optical character recognition (OCR) on binarized text using the BBN Byblos Offline Handwritten text recognition (OHR) system.
12 citations
16 Aug 1998
TL;DR: This method provides a solution to the crucial issue of assigning reliable cost to the edges of the segmentation graph in the popular over-segmentation followed by dynamic programming approach for word recognition.
Abstract: We present a method of combining multiple classifiers for optimizing word recognition. The proposed method combines the results of individual classifiers in such a way that the correct word is more likely to be hypothesized. This method provides a solution to the crucial issue of assigning reliable cost to the edges of the segmentation graph in the popular over-segmentation followed by dynamic programming approach for word recognition. Three combination functions are proposed and implemented. Experiments show that proposed method has a significant improvement on the word recognition accuracy.
12 citations
20 Dec 2005
12 citations
01 Aug 1992
TL;DR: A robust algorithm for offline cursive script recognition that is not requiring explicit word training yet is able to recognize many handwriting styles and is being successfully tested on a database of handwritten words extracted from live mail with dictionary sizes of up to 300 words.
Abstract: A robust algorithm for offline cursive script recognition is described. The algorithm uses a generate-and-test paradigm to analyze cursive word images. The generate phase of the algorithm intelligently segments the word after analyzing certain structural features present in the word. The test phase determines the most likely character candidates among the segmentation points by using a recognition algorithm trained on generalized cursive letter shapes. In a sense, word recognition is done by sliding a variable sized window across the word looking for recognizable characters and strokes. The output of this system is a list of all plausible interpretations of the word. This list is then analyzed by a two-step contextual post- processor which first matches all of the interpretations to a supplied dictionary using a string matching algorithm. This eliminates the least likely interpretations. The remaining candidates are then analyzed for certain character spatial relationships (local reference line finder) to finally rank the dictionary. The system has the advantage of not requiring explicit word training yet is able to recognize many handwriting styles. This system is being successfully tested on a database of handwritten words extracted from live mail with dictionary sizes of up to 300 words. Planned extensions include developing a multilevel generate-and-test paradigm which can handle any type of handwritten word.
12 citations