Artificial Neural Network for Cursive Handwriting Recognition System 
Aditi Gupta
Aditi Gupta*, Assistant Professor, Department of Computer Science, DAV College for Boys, Hathi Gate, Amritsar. India.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 877-880 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9866069520/2020©BEIESP | DOI: 10.35940/ijeat.E9866.069520
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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: Cursive Handwriting acknowledgment is an extremely testing zone because of the one of a kind styles of composing starting with one individual then onto the next. Right now, disconnected cursive composing character acknowledgment framework is portrayed utilizing an Artificial Neural Network. The highlights of every character written in the information are extricated and afterward sent to the neural system. Informational collections, having writings of various individuals are utilized in making framework. The suggested acknowledgment framework yields elevated steps of exactness when contrasted with the ordinary methodologies right now. This framework can effectively perceive cursive messages and convert them into auxiliary structure.
Keywords: Image Processing, Feature extraction, neural networks, Cursive handwriting, Classification