Vehicle Number Management using Hbase
Chetan Pandey1, Amit Juyal2, Poonam Verma3, Kamred Udham Singh4
1Chetan Pandey, Graphic Era Hill University, Dehradun, Uttarakhand.
2Amit Juyal , Graphic Era Hill University, Dehradun, Uttarakhand.
3Poonam Verma , Graphic Era Hill University, Dehradun, Uttarakhand.
4Kamred Udham Singh , Graphic Era Hill University, Dehradun, Uttarakhand.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3382-3388 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6706098319/2019©BEIESP | DOI: 10.35940/ijrte.C6706.118419

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 (

Abstract: Intelligent Monitoring and Recording System (IMRS) is being used in many fields like Aviation Traffic control, Transportation, Real Estate, Medical Science and more. One of the IMRS system is Vehicle Traffic on roads. Intelligent Transportation System (ITS) is an important part of IMRS which is used to collect and analyze the statistics related to the Vehicle Traffic. This paper gives an insight of the ITS by using open source tools which are easy to implement. Large datasets of vehicle information has been taken into the experiments of this paper. Utilizing different digital image processing techniques, we have extracted vehicle number from the number plates. Technically, this paper is based on ITS in which two main features has been designed, first is to store Vehicle related information in the Hbase. Another feature is to retrieve the data, on the basis of the vehicle number, from the database giving the details of the vehicle including Road Tax, Insurance and Stolen status and also inform the possessor about the invalidity of Insurance, Registration Certificate (RC) and License. Time and accuracy are two challenges in performing the IMRS in a real life scenario. It has been observed that the proposed system is 27% faster than Automatic Number Plate Recognition (ANPR) on the basis of two comparative parameters i.e. Precision and Recall.
Keywords: Hadoop, HDFS, HBase, OpenCV, OCR, Tess4J.
Scope of the Article: Information Ecology and Knowledge Management.