# Iranis: A Large-scale Dataset of Farsi License Plate Characters

arXiv: https://arxiv.org/abs/2101.00295 · HTML: https://mlsanity.com/paper/2101.00295/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [DDI-100: Dataset for Text Detection and Recognition](https://mlsanity.com/paper/1912.11658/) (2019)

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

- [IR-LPR: Large Scale of Iranian License Plate Recognition Dataset](https://mlsanity.com/paper/2209.04680/) (2022)
- [Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition](https://mlsanity.com/paper/2209.14448/) (2022)

## Abstract (arXiv)

Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.