Deep Features based Multilingual Text Detection and Recognition in Video Frames
Sunil C1, K S Raghunandan2, H K Chethan3, G Hemantha Kumar4
1Sunil C*, Department of Computer Science and Engineering, MIT Mysore, India.
2K S Raghunandan, Dept. of Studies in Computer Science, University of Mysore, Mysore, India.
3H K Chethan, Department of Computer Science and Engineering, MIT Mysore, India.
4G Hemantha Kumar, Dept. of Studies in Computer Science, University of Mysore, Mysore, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9420-9429 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9726118419/2019©BEIESP | DOI: 10.35940/ijrte.D9726.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: 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.
Keywords: Text Detection, Text Recognition, Deep Neural Network.
Scope of the Article: Image Processing and Pattern Recognition.