  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.
