Telugu Word Image Retrieval using Deep learning Convolutional Neural Networks
Kesana Mohana Lakshmi1, Tummala Ranga Babu2 

1Kesana Mohana Lakshmi, Research Scholar, Dept. of Engineering and Communication Engineering, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur.
Department of Electronics and Communication Engineering, CMR Technical Campus, Hyderabad, Telangana, India
2Tummala Ranga Babu, Dept. of Engineering and Communication Engineering, RVR & JC College of Engineering, Guntur.

Manuscript received on 11 March 2019 | Revised Manuscript received on 15 March 2019 | Manuscript published on 30 July 2019 | PP: 5860-5865 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3776078219/2019©BEIESP | DOI: 10.35940/ijrte.B3776.078219
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Abstract: Telugu word image retrieval (TWIR) is a still challenging task due to the structure complexity of Telugu word image. An efficient TWIR system can be implemented by a holistic representation of word image that comprises of every possible extracted feature. Further, it is also required to retrieve more relevant word images even there is a noisy query word image. Here, it is proposed an efficient TWIR system that utilizes deep learning convolutional neural networks (DL-CNN) to extract the feature map from the query and database word images. In addition, principal component analysis (PCA) is employed to compute the principal features form the feature map and pairwise hamming distance is considered as a similarity metric to retrieve most relevant Telugu word images from the database. Extensive simulation analysis disclosed that proposed TIWR system obtained a superior performance over conventional TIWR systems in terms of mean average precision (mAP) and mean average recall (mAR).
Index Terms: Telugu Word Image Retrieval, Deep Learning Convolutional Neural Networks, Principal Component Analysis, Hamming Distance, Precision and Recall.

Scope of the Article: Deep Learning