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

TL;DR

Iranis is a comprehensive large-scale dataset of over 83,000 images of Farsi license plate characters, designed to advance deep learning-based recognition systems for law enforcement and surveillance.

Contribution

The paper introduces Iranis, the first large-scale publicly available dataset of Farsi license plate characters, with detailed annotations and a baseline recognition performance using YOLO v.3.

Findings

Dataset contains 83,000+ images with diverse conditions.

Manual annotations enable effective training for detection and classification.

Baseline YOLO v.3 performance established for future research.

Abstract

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.