International Journal for Research in Applied Science & Engineering Technology (IJRASET)

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Automated Vehicle Number Plate Detection and Recognition

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue I Jan 2023- Available at www.ijraset.com

IV. LITERATURE REVIEW

S. No Title Author Approach

Advantages Challenge

1 Vehicle Number Plate Detection System for Indian Vehicles

Hanit Karwal, Akshay Girdhar[3]

Proposed an efficient algorithm for the recognition of position of characters .

Algorithm was quite efficient, addressed the problem of scaling with decent amount of accuracy.

Less number of sample taken.

2 Characters feature based Indian vehicle license plate detection and recognition

Sudhir K. Ingole, Shital B. Gundre[2]

Based on recognisingthe characters on the license plate, using an adaptive preprocessing method.

Robust in extricatingsingle line number plate.

Failed to segment double row number plate

3 Automatic Number Plate Recognition (ANPR) system for Indian conditions

Prathamesh Kulkarni, Ashish Khatri, Prateek Banga, Kushal Shah[4]

Comprised of a mixture of algorithms, for example, Feature based number plate Localization for finding the tag, Image Scissoring for character division and factual element extraction for character acknowledgement.

The system recognized single and double row license plates with an accuracy of 82%.

The major restrictions faced by them in their work were attributed to parameter such as speed of the vehicle and slew in the image.

4 Indian car Number Plate Recognition using Deep Learning

R Naren Babu, V Sowmya, K P Soman [5]

Proposed an efficient license plate recognition model for different illumination and camera angle views. Training of the manually collected number plate dataset was carried out by employing the YOLO V3.

Overcame the previous restrictions that were faced by people in their work done prior.

Failed to deal with the similarity problem between 0 and o and realized the need to apply image processing techniques for better character recognition.

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue I Jan 2023- Available at www.ijraset.com

V. DATAFLOW DIAGRAM

In this Data Flow Diagram, we show how the flow of data in our system.

VI.

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue I Jan 2023- Available at www.ijraset.com

VII. CONCLUSION

This project performs mainly four tasks. The first task is to input an image of the car and this will happen with help of the webcam of the computer for the prototype. When the image is fed the image is enhanced in quality. The enhancement is done in the resolution and the thresholding. The image is constraint to a fixed image frame size. After the enhancement the image is processed to segment the number plate from the full to segment all the characters in the picture in the form of Text and then it can be stored in a database or can be displayed as in this prototype. The project is designed so that we can understand the technology used in now-adays Automatic license plate systems and OCR systems used in most of the developed countries like Germany, France, Singapore, Japan, etc.

References

[1] P. Kulkarni, A. Khatri, P. Banga and K. Shah, "Automatic Number Plate Recognition (ANPR) system for Indian conditions," 2009 19th International Conference Radioelektronika, Bratislava, 2009, pp. 111- 114, doi: 10.1109/RADIOELEK.2009.5158763.

[2] R. Naren Babu, V. Sowmya and K. P. Soman, "Indian Car Number Plate Recognition using Deep Learning," 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, Kerala, India, 2019, pp. 1269-1272, doi: 10.1109/ICICICT46008.2019.8993238.

[3] B. S. Prabhu, S. Kalambur and D. Sitaram, "Recognition of Indian license plate number from live stream videos." 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, 2017, pp. 2359-2365, doi: 10.1109/ICACCI.2017.8126199..

[4] J. Singh and B. Bhushan, "Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract," 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2019, pp. 347-352, doi: 10.1109/ICCCIS48478.2019.8974469.

[5] M. Hassaballah, M. A. Kenk, K. Muhammad and S. Minaee, "Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3014013.

[6] G. Hsu, A. Ambikapathi, S. Chung and C. Su, "Robust license plate detection in the wild," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 2017, pp. 1-6, doi: 10.1109/AVSS.2017.8078493.

[7] J. Špaňhel, J. Sochor, R. Juránek, A. Herout, L. Maršík and P. Zemčik, "Holistic recognition of low quality license plates by CNN using track annotated data," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 2017, pp. 1-6, doi: 10.1109/AVSS.2017.8078501.

[8] S. Rahati, R. Moravejian, E. M. Kazemi and F. M. Kazemi, "Vehicle Recognition Using Contourlet Transform and SVM," Fifth International Conference on Information Technology: New Generations (itng 2008), Las Vegas, NV, 2008, pp. 894-898, doi: 10.1109/ITNG.2008.136.

[9] S. Kaul, G. Joshi and A. Singh,"Automated Vehicle Detection and Classification Methods", in press.

[10] K. R. Soumya, A. Babu and L. Therattil, "License plate detection and character recognition using contour analysis", International Journal of Advanced Trends in Computer Science and Engineering, 2014.