Automatic Handwritten Devanagari Text Generation in Marathi Styles using Ant Miner Algorithm
Vajid Khan1, Yogesh Kumar Sharma2
1Mr. Vajid N Khan*, Research Scholar, Department of Computer science and engineering ,Shree JJT University, Rajsthan , India.
Dr. Yogesh Kumar Sharma, Head of Department of computer science and engineering, Shree JJT University, Rajsthan, India
Manuscript received on November 15, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 2541-2551 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7163129219/2019©BEIESP | DOI: 10.35940/ijitee.B7163.129219
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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: The Devanagari scripts forms the backbone of the writing system of several Indian languages includes Hindi, Sanskrit and Marathi. With the increased demand, exploration and globalization of digital Devanagari documents, different printed and handwritten document recognition techniques have involved since last two decades. In literature many methods of Devanagari script recognition have been used but it is not able to attain the best results in recognition. Hence, in this paper is proposed Ant Miner Algorithm (AMA) for recognition and text generation of handwritten Devanagari Marathi Scripts. The proposed method recognition process is working with the four different stages such as pre-processing, segmentation, feature extraction and recognition with text generation. The first stage pre-processing is consists of skew correction, noise removal and binarization. The second stage is segmentation that contains the line segmentation, word segmentation and character segmentation. The third stage is feature extraction method it contains four methods such as Scale Invariant Feature Transform (SIFT), Linear Discriminant Analysis (LDA), Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP). The final stage is recognition and text generation with attain with the help of AMA algorithm. It works based on the two phases such as training and testing phase. The proposed method is implemented in the python platform and it compared with the Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN). The performance of the proposed method is analysed with statistical measurements of accuracy, precision and recall.
Keywords: Devanagari Script, Marathi, AMA, Feature Extraction, Pre-processing, Segmentation.
Scope of the Article: Algorithm Engineering