Telugu Character Recognition Using Adaptive Fuzzy Membership Functions With Adaptive Genetic Algorithm Based Techniques
V. V. Satyanarayana Tallapragada1, V. Sireesha2, G. V. Pradeep Kumar3
1V. V. Satyanarayana Tallapragada*, Associate Professor, Department of ECE, Sree Vidyanikethan Engineering College, Tirupati, India.
2V. Sireesha, Associate Professor, Geethanjali Institute of Science and Technology (GIST), Nellore, India.
3G. V. Pradeep Kumar, Assistant Professor, Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 10 August 2019. | Manuscript published on 30 September 2019. | PP: 3092-3097 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4973098319/2019©BEIESP | DOI: 10.35940/ijrte.C4973.098319
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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: A novel Telugu character recognition technique is proposed in this paper where the given Telugu handwritten document is processed by normalizing the document and removing the noise. Then slant detection followed by correction process is conceded using the bilinear interpolation method to get more accurate result. Thus the de-skewed documents text lines and characters are separated by making use of Adaptive Histogram Equalization (AHE). In the next stage, the characteristics of the segmented characters are mined with the help of the zoning method. In zoning method, an adaptive fuzzy membership function will be developed by the Adaptive Genetic Algorithm (AGA). By using AGA in zoning method the characteristics are mined from the separated characters. The mined structures are applied to the Feed Forward Back Propagation Neural Network (FFBNN) for accomplishing the learning process. During testing, more number of handwritten segmented Telugu characters will be set to the FFBNN to verify whether the input character is recognized or not. Thus, the proposed method has given more accurate recognition results by using our proposed adaptive fuzzy membership function with AGA method. The proposed method performance is evaluated by getting more number of handwritten Telugu documents and compared with the GA-FFBNN and FFBNN.
Keywords: Adaptive Histogram Equalization, Feed-forward Back-Propagation Neural Network, Adaptive Genetic Algorithm, Zoning, Bilinear Interpolation.
Scope of the Article: Pattern Recognition