Text Recognition with Artificial Neural Networks and OpenCV
A. Sunitha Nandhini1, R. Sujatha2, S. Padmavathi3, S. Sampeter4
1Ms. A. Sunitha Nandhini, Department of Computer Science Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu.
2Ms. R. Sujatha, Department of Computer Science Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu.
3Ms. S. Padmavathi, Department of Computer Science Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu.
4S. Sampeter, Department of Computer Science Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu.
Manuscript received on 16 March 2019 | Revised Manuscript received on 21 March 2019 | Manuscript published on 30 July 2019 | PP: 5525-5528 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3261078219/19©BEIESP | DOI: 10.35940/ijrte.B3261.078219
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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: Recognizing text in images has received attention recently. Traditional systems during this space have relied on elaborating models incorporating rigorously hand-designed options or giant amounts of previous information. This paper proposed by taking a different route and combines the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a standard framework to coach highly accurate character recognizer and text detector modules. The recognition pipeline of scanning, segmenting, and recognition is examined and delineated completely
Keywords: Artificial Neural Network, Handwriting Recognition, Segmentation.
Scope of the Article: Pattern Recognition