Broken Character Recognition using Connected Components and Convolutional Neural Network
Roshan D Suvaris1, S Sathyanarayana2
1Roshan D Suvaris, Research Scholar, Assistant Professor, Bharathiar University, Coimbatore AIMIT St Aloysius College Mangalore (Karnataka), India.
2Dr. S Sathyanarayana, First Grade Womens College Mysore (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 46-49 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10110285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1011.0285S20
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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 broken characters in scanned and ancient scanned text document is not easy because the characters may be broken and unclear. Many researches have been carried to recognize these broken characters. In this research paper we have described a new broken characters recognition method for English text documents only. The proposed method uses a hybrid approach which uses connected component concepts and convolutional neural network to identify the broken characters. The input to the approach is scanned or ancient text document which contains unclear text that is difficult to recognize and hence our new proposed methodology will recognize these characters with greater accuracy and it will give the recognized characters to the user. The projected technique has attained a precision up to 92% in recognition.
Keywords: Connected Components, Convolutional Neural Network, Image Processing.
Scope of the Article: Pattern Recognition