Text Extraction and Recognition in Natural Scene Images using Contourlet Transform and PNN
Shivananda V. Seeri1, P. S. Hiremath2, J. D. Pujari3, Prakashgoud Patil4
1Shivananda V. Seeri, Department of MCA, KLE Technological University, Hubballi (Karnataka), India.
2P. S. Hiremath, Department of MCA, KLE Technological University, Hubballi (Karnataka), India.
3J. D. Pujari, Department of Information Science & Engineering, S.D.M. College of Engineering, & Technology, Dharwad (Karnataka), India.
4Prakashgoud Patil, Department of MCA, KLE Technological University, Hubballi (Karnataka), India.
Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 83-91 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10561292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1056.1292S19
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© 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: Of late, the rapid development in the technology and multimedia capability in digital cameras and mobile devices has led to ever increasing number of images or multi-media data to the digital world. Particularly, in natural scene images, the text content provides explicit information to understand the semantics of images. Therefore, a system developed for extracting and recognizing texts accurately from natural scene images, in real-time, has significant relevance to numerous applications such as, assistive technology for people with vision impairment, tourist with language barrier, vehicle number plate detection, street signs, advertisement bill boards, robotics, etc. The extraction of the texts from natural scene images is a formidable task due to large variations in character fonts, styles, sizes, text orientations, presence of complex backgrounds and varying light conditions. The main focus of this research paper is to propose a novel hybrid approach for automatic detection, localization, extraction and recognition of text in natural scene images with cluttered background. Firstly, image regions with text are detected using edge features (GLCM) extracted from Contourlet transformed image and SVM (Support Vector Machine) classifier. Secondly, horizontal projection is applied on text regions for segmenting lines and vertical projection is applied on each text line for segmenting characters. The proposed method for text extraction has produced the precision, recall, F-Score and accuracy of 98.50%, 90.85.62%, 95.00%, and 89.90%, respectively. And, these results prove that the proposed method is efficient. Further, the so extracted characters are processed for recognition using contourlet transform and Probabilistic Neural Network (PNN) classifier. The computed features are moment invariants. Only the English script is considered for the experimentation. The proposed character recognition method has accuracy of 79.07%, which is higher in comparison to accuracy of 75.15% obtained by KNN (K-Nearest Neighbors) classifier.
Keywords: Scene Image, Text Extraction, Character Recognition, Contourlet Transform, PNN.
Scope of the Article: Text Mining