# Indian Licence Plate Dataset in the wild

arXiv: https://arxiv.org/abs/2111.06054 · HTML: https://mlsanity.com/paper/2111.06054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector](https://mlsanity.com/paper/1802.09567/) (2018)
- [LPRNet: License Plate Recognition via Deep Neural Networks](https://mlsanity.com/paper/1806.10447/) (2018)

## Built on by (most cited first, 2 of 2)

- [Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation](https://mlsanity.com/paper/2211.13194/) (2022)
- [Global License Plate Dataset](https://mlsanity.com/paper/2405.10949/) (2024)

## Abstract (arXiv)

Indian Licence Plate Detection is a problem that has not been explored much at an open-source level.There are proprietary solutions available for it, but there is no big open-source dataset that can be used to perform experiments and test different approaches.Most of the large datasets available are for countries like China, Brazil, but the model trained on these datasets does not perform well on Indian plates because the font styles and plate designs used vary significantly from country to country.This paper introduces an Indian license plate dataset with 16192 images and 21683 plate plates annotated with 4 points for each plate and each character in the corresponding plate.We present a benchmark model that uses semantic segmentation to solve number plate detection. We propose a two-stage approach in which the first stage is for localizing the plate, and the second stage is to read the text in cropped plate image.We tested benchmark object detection and semantic segmentation model, for the second stage, we used lprnet based OCR.