Synthesizing and Imitating Handwriting using Deep Recurrent Neural Networks and Mixture Density Networks

Handwriting is a skill developed by humans from the very early stage in the order to represent his/her thoughts visually using letters and making meaningful words and sentences. Every person improves this skill by practicing and develops his/her own style of writing. Because of the distinctiveness of handwriting style, it is frequently used as a measure to identify a forgery. Even though the applications of synthesizing of handwriting is less, this problem can be generalized and can be functionally applied to other more practical problems. Synthesizing the handwriting is a quite complicated task to achieve. Deep recurrent neural networks specifically Deep LSTM cells can be used along with a Mixture Density Network to generate artificial handwriting data. But using this model we can only generate random handwriting styles which are being hallucinated by the model. Mimicking a specific handwriting style is not so efficient with this model. Mimicking or imitating a specific handwriting style can have an extensive variety of applications like generating personalized handwritten documents, editing a handwritten document by using the similar handwriting style and also it is extended to compare handwriting styles to identify a forgery. A web prototype is developed along with the model to test the results where the user can enter the text input and select handwriting style to be used. And the application will return the handwritten document containing input text mimicking the selected handwriting style. The application will also provide a way to fine-tune the handwriting styles by changing few parameters.