Indian Licence Plate Dataset in the wild

TL;DR

This paper introduces a new open-source dataset of Indian license plates with annotations and benchmarks a two-stage detection and recognition approach using semantic segmentation and OCR, addressing the lack of Indian-specific data.

Contribution

The paper provides the first large-scale Indian license plate dataset and a benchmark model employing a two-stage detection and recognition pipeline.

Findings

Semantic segmentation effectively localizes license plates.

LPRNet-based OCR accurately reads characters.

Benchmark results establish a baseline for future research.

Abstract

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.