Realistic Handwriting Generation using Generative Adversarial Networks (Rnn)
Rajakumar K1, Spreeha Dutta2, Bipsa Nayak3, Bindhiya N Koliwad4
1Rajakumar k*, Computer Science, Vellore Institue of Technology, Vellore, India.
2Spreeha Dutta*, Computer Science, Vellore Institue of Technology, Vellore, India.
3Bipsa Nayak*, Computer Science with specialization in Bio informatics,Vellore Institue of Technology , Vellore, India.
4Bindhiya N Koliwad*, Computer Science with specialization in Bio informatics, Vellore Institue of Technology , Vellore, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2049-2052 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2761059120/2020©BEIESP | DOI: 10.35940/ijrte.A2761.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Generating handwritings of different kinds is quite a challenging task, an area in which not much work has been done yet. Though there has been substantial research done in the area of text recognition, the opposite of handwriting generation. Handwriting generation can prove to be extremely useful for children from blind schools where their speech can get converted into text and be used to generate handwritings of different kinds for them. Handwriting generation also has an important role in field of captcha generation. Our study exhibits in what way recurrent neural networks (RNN) of the type Long Short Term Memory (LSTM) could be used in order to create a composite sequence with structure covering a long range. We propose to use that the Generative Adversarial Network algorithm can be used to generate more realistic handwriting styles with better accuracy than other algorithms. Here, we will be trying to predict one point of data at a time. Our approach is shownfor text, where the type of data is discrete. It can also be used for online handwriting, that is real-valued data. It will then be further drawn out to handwriting generation. The created network will be conditioning its predictions based on a sequence of text. We will be using the resulting system to generate highly realistic cursive handwriting in a wide variety of styles. Experiments that have been carried out on online handwriting databases that are public predict that the method that has been proposed can be used to achieve satisfactory performance, the resultant writing samples achieved a high level of similarity with original samples of handwriting.
Keywords: Recurrent Neural Networks, Long Short Term Memory, Generative Adversarial Networks, handwriting generating, online handwriting.
Scope of the Article: Neural Networks