Open AccessTelugu Word Image Retrieval using Deep learning Convolutional Neural Networks
Author(s) -
K. Mohana Lakshmi,
Tummala Ranga Babu
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3776.078219
Subject(s) - telugu , computer science , artificial intelligence , word (group theory) , convolutional neural network , pattern recognition (psychology) , feature (linguistics) , image retrieval , principal component analysis , metric (unit) , deep learning , visual word , similarity (geometry) , pairwise comparison , feature extraction , precision and recall , image (mathematics) , mathematics , linguistics , philosophy , operations management , geometry , economics
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).