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Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.
In this paper, we propose a fully automatic system for the transcription alignment of historical documents. We introduce the ‘Statuti del Doge Tiepolo’ data that include images as well as transcriptio
This paper proposes a system for text-independent writer identification based on Arabic handwriting using only 21 features. Gaussian Mixture Models (GMMs) are used as the core of the system. GMMs prov