A Hybrid Machine Learning based Approach for Hindi License Plate Recognition
Nikita Singh1, Tarun Kumar2, Sharbani Mallick3
1Nikita Singh, Centre For Skill & Entrepreneurship Development, Banasthali University, Banasthali, Jaipur, India.
2Tarun Kumar, Centre For Skill & Entrepreneurship Development, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, India.
3Sharbani Mallick, Centre For Skill & Entrepreneurship Development, Dr. B.R Ambedkar Institute of Technology Port Blair, India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 02 June 2019 | PP: 548-553 | Volume-8 Issue-7S2 May 2019 | Retrieval Number: G10930587S219/19©BEIESP
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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: In the present era of revolutionary techniques, the developed countries are using various technologies to provide the better quality of social life. In these technologies the automatic number plate recognition is playing vital role in intelligent transportation management. Most state-of-the-art ANPR are compatible with language specific license plates specifically English. The langue-specific characteristics limit the competence of ANPR where more than one language is used in license plates. In this paper a system for recognition of Hindi license plates is proposed. The proposed approach is based on hybrid machine learning techniques such as support vector machine and artificial neural network. These two different machine learning approachs are integrated in order to improve the performance of system. The average license plate recognition accuracy of proposed approach is above 96% in recognition of Hindi license plates.
Keywords: Character Segmentation, Character Recognition, HOG, SVM, ANN.
Scope of the Article: Machine Learning