Soft Computation Based Topographic Map Legend Understanding Prototype System
Nikam Gitanjali Ganpatrao1, Jayanta Kumar Ghosh2
1Nikam Gitanjali Ganpatrao, Geomatics Engineering Section, Deaprtment of Civil Engineering, Indian Institute of Technology, Roorkee, Uttaranchal, India.
2Jayanta Kumar Ghosh, Associate Professor, Deaprtment of Civil Engineering, Indian Institute of Technology, Roorkee, Uttaranchal, India.
Manuscript received on February 04, 2013. | Revised Manuscript received on February 26, 2013. | Manuscript published on March 05, 2013. | PP: 116-120 | Volume-3 Issue-1, March 2013. | Retrieval Number: A1310033113/2013©BEIESP
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© The Authors. Published By: 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: The goal of the study is to devise an intelligent system to understand topographic map automatically. This paper explains the design of a system to automatically interpret information from scanned Indian topographic map legends set. A method based on perception of shape provides a collective understanding of size, form and orientation as that of human psycho-visual approach, is required towards development of a topographic map legends understanding system. The fundamental of the system are map legend analysis algorithms- Edge detection algorithm and line thinning algorithm to extract patterns and shape features from images of scanned topographic map legends and describe it as primitives which is building entity of shape of legend. An approach is based on feature extraction model and back propagation neural network which allows efficient and coherent management of map legends, recognition processes, recognition results. The system incorporates shape feature and uses back propagation neural network for recognition. The experimental results show that developed system performs well in recognition and understanding of map legends.
Keywords: Back propagation neural network, Edge detection, Legend primitives, Map understanding, Syntactic pattern recognition, Thinning algorithm.