Learning Enhancement of Online Handwritten Telugu Character Modeling for Various Features Sets
Goda Srinivasarao1, Rajeswara Rao Ramisetty2
1Goda Srinivasarao, Associate Professor, Department of CSE, PACE Institute of Technology & Sciences.
2Dr. Rajeswara Rao Ramisetty, Professor, Department of CSE, JNTUK-UCEV-Vizianagaram.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 5467-5470 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3087109119/2019©BEIESP | DOI: 10.35940/ijeat.A3087.109119
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Feature extraction plays vital role in online hand written character recognition. Local Features captured through co-ordinate system approach plays significant role in determining the online telugu character recognition. In this paper, we have instigated the performance of various features using Artificial Neural Networks( ANNs). ANN model is tested with various combination such as (x,y) co-ordinates , pen-up and pen-down (Δx,Δy) , (Δ 2x Δ2y), Finally it is observed that (Δ2x Δ2y) features have given better accuracy. 95.18 % performance is obtained for 300 epochs for 52 Telugu characters. The database used for the study is HP-online Telugu database.
Keywords: Pen-down (Δx,Δy) , (Δ 2x Δ2y), Finally it is observed that (Δ2x Δ2y) features have given better accuracy. 95.18 % performance.