Open AccessDeep Features based Multilingual Text Detection and Recognition in Video Frames
Author(s) -
Sunil Retmin Raj C.,
K. S. Raghunandan,
H. K. Chethan,
G. Hemantha Kumar
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.d9726.118419
Subject(s) - artificial intelligence , computer science , sliding window protocol , pattern recognition (psychology) , text detection , convolutional neural network , window (computing) , computer vision , cluster analysis , robustness (evolution) , image (mathematics) , operating system , biochemistry , chemistry , gene
One of the major causes for achieving poor text detection results in video frames are complex background, illumination and de-blurring of the frames and it is also important challenges for the researchers to overcome such problems. Therefore, motivated from this kind of observation from recent survey, we propose a text detection method based on Deep Neural Networks known as TextBoxes which is capable of detecting text in video frames with improved performance when compared to state-of-the-art techniques. In parallel this we also propose a Text candidate detection for video frames and scene images by extracting words based on Automatic Window Detection by making use of Discrete Wavelet Transform (DWT) with the sliding window for extracting high frequency sub bands for each sliding window. K-means clustering technique has been used to obtain the text components and to decrease the background complexity and noise. Six-layer convolutional neural network model has been designed to recognize the text in multilingual images. Experiments for text detection are done on our own multilingual South Indian database, ICDAR-2015 Videos, YVT videos, SVT, and MSRA Scene datasets and demonstrated in terms of Recall, Precision and F-measure and for recognition ICDAR-2015 Videos, ICDAR 2011 and SVT scene images