Nagulu Hemanth, Gangisetti Praveen Sai, Gunji Lakshmi Gopinath Sai Kumar, Kusampudi Mohan Krishna, Inunganbi Sanasam, Venkata Vara Prasad Padyala
Script, Cursive writing, Machine learning (ML), Deep learing(DL).
Recognition of handwritten documents and text is one of the most difficult tasks in the field of pattern recognition. It contains a large number of variables to recognize words, alphabets, digits and characters and some strokes in handwriting are mandatory. Our paper focuses on handwritten text which is manually written by humans. In this process we use artificial neural network, convocational network, machine learning and deep learning to process the handwritings. Basically handwritten recognition is of two types. One is manual and another one is online. The manual one is done by this human observation and the online one is done by artificial intelligence. This method is one of the most difficult ones because of the slant and uniqueness. In these patterns we look for specific strokes and patterns in which we can classify the words which affect the system requirements. By this paper we can help mini researchers who are trying to improve the technology in this sector so they can reduce the error rate and increase the efficiency of the model. The main difficulty in this process is to identify the letters which are written in cursive writing and block writing and also let us which overlap on each other and combine.
Article Details
Unique Paper ID: 159405

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 1135 - 1140
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