  Indian vehicle number plates have wide variety in terms of size, font, script
and shape. Development of Automatic Number Plate Recognition (ANPR) solutions
is therefore challenging, necessitating a diverse dataset to serve as a
collection of examples. However, a comprehensive dataset of Indian scenario is
missing, thereby, hampering the progress towards publicly available and
reproducible ANPR solutions. Many countries have invested efforts to develop
comprehensive ANPR datasets like Chinese City Parking Dataset (CCPD) for China
and Application-oriented License Plate (AOLP) dataset for US. In this work, we
release an expanding dataset presently consisting of 1.5k images and a scalable
and reproducible procedure of enhancing this dataset towards development of
ANPR solution for Indian conditions. We have leveraged this dataset to explore
an End-to-End (E2E) ANPR architecture for Indian scenario which was originally
proposed for Chinese Vehicle number-plate recognition based on the CCPD
dataset. As we customized the architecture for our dataset, we came across
insights, which we have discussed in this paper. We report the hindrances in
direct reusability of the model provided by the authors of CCPD because of the
extreme diversity in Indian number plates and differences in distribution with
respect to the CCPD dataset. An improvement of 42.86% was observed in LP
detection after aligning the characteristics of Indian dataset with Chinese
dataset. In this work, we have also compared the performance of the E2E
number-plate detection model with YOLOv5 model, pre-trained on COCO dataset and
fine-tuned on Indian vehicle images. Given that the number Indian vehicle
images used for fine-tuning the detection module and yolov5 were same, we
concluded that it is more sample efficient to develop an ANPR solution for
Indian conditions based on COCO dataset rather than CCPD dataset.
