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This paper present a query-by-example word spotting in handwritten Arabic documents, based on Harris detector and Scale Invariant Feature Transform (SIFT), without using any text word or line segmentation approach, because any errors affect to the subsequent word representations. First, the interest points are automatically extracted from the images using Harris detector, then, we use SIFT descriptor...
This paper attempts to deal with the problems of query-by-example word spotting in handwritten Arabic document. This operation needs a lot of time and effort to do manual work. For this, we propose a fully non supervised methodology dedicated to the word spotting system, without using any text word or line segmentation step, because any segmentation errors of the document affect the subsequent word...
In this paper, we present techniques for unsupervised adaptation of stochastic segment models to improve accuracy on large vocabulary offline handwriting recognition (OHR) tasks. We build upon our previous work on stochastic segment modeling for Arabic OHR. In our previous work, stochastic character segments for each n-best hypothesis were generated by a hidden Markov model (HMM) recognizer, and then...
In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists...
We propose a new algorithm for segmentation of off-line handwritten Arabic words. The algorithm segments the connected letters to smaller segments each of which contains no more than three letters. Each letter may be segmented to at most five pieces. In addition to improving the recognition of Arabic words, another potential application of the proposed segmentation method is to build lexicon of small...
In this paper, we present a novel approach for incorporating structural information into the hidden Markov modeling (HMM) framework for offline handwriting recognition. Traditionally, structural features have been used in recognition approaches that rely on accurate segmentation of words into smaller units (sub-words or characters). However, such segmentation based approaches do not perform well on...
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