Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features
Gururaj Mukarambi1, B. V. Dhandra2, Satishkumar Mallappa3 

1Gururaj Mukarambi, Symbiosis Institute of Computer Studies and Research, Symbiosis International Deemed University, Pune, Maharashtra, India.
2B.V. Dhandra, Symbiosis Institute of Computer Studies and Research, Symbiosis International Deemed University, Pune, Maharashtra, India,
3Satishkumar Mallappa, Department of P.G. Studies and Research in Computer Science Gulbarga Unviersity Kalaburagi, Karnataka, India. 

Manuscript received on 15 March 2019 | Revised Manuscript received on 21 March 2019 | Manuscript published on 30 July 2019 | PP: 2988-2994 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2713078219/2019©BEIESP | DOI: 10.35940/ijrte.B2713.078219
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Abstract: Most of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level (2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.
Index Terms: KNN, LBP, SFTA, SVM., TTBD.

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