  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.
