Historical documents are essentially formed of handwritten texts that exhibit a variety of perceptual environment complexities.
Historical documents are essentially formed of handwritten texts that exhibit a variety of perceptual environment complexities. The cursive and connected nature of text lines on one hand and the presence of artefacts and noise on the other hand hinder achieving plausible results using current image processing algorithm. In this proposal, we would like to explore new algorithms which should allow for pattern spotting in scanned handwritten historical documents. The algorithms must be highly scalable to be able to process very large datasets (i.e.,Bigdata).
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