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The objective of this thesis is to learn the handwriting of a person and artificially synthesize text that is indistinguishable from that person's handwriting. The thesis builds this system for writer specific artificial handwriting synthesis from scratch. It builds a handwriting sampling text which aims at acquiring maximum individualistic handwriting traits; minimizing sampling text word count; retaining fidelity of natural handwriting. It analyzes image processing techniques to reduce image acquisition noise and extract information relevant to modelling handwriting. It shows how the handwritten stroke and letter shape is modelled. It demonstrates how writer specific realistic handwriting is synthesized by pseudo-random variations in the parameters of the model of that writer's handwriting. Finally, it indicates various avenues for further research and development to scale the system to higher complexity to synthesize more realistic handwriting.