Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents
Mohamed Cheriet, Reza Farrahi Moghaddam, Ehsan Arabnejad and Guoqiang Zhong, Synchromedia Laboratory for Multimedia Communication in Telepresence, École de technologie supérieure, Montreal, QC, Canada H3C 1K3, mohamed.cheriet@etsmtl.ca rfarrahi@synchromedia.ca earabnejad@synchromedia.ca guoqiang.zhong@synchromedia.ca
Abstract
In this work, a recognition approach applicable at the letter block (subword) level for Arabic manuscripts is introduced. The approach starts with the binary images of the letter block to build their input representation, which makes it highly objective and independent of the designer. Then, using two different manifold learning ...
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