  The issue of Automatic License Plate Recognition (ALPR) has been one of the
most challenging issues in recent years. Weather conditions, camera angle of
view, lighting conditions, different characters written on license plates, and
many other factors are among the challenges for the issue of ALPR. Given the
advances that have been made in recent years in the field of deep neural
networks, some types of neural networks and models based on them can be used to
perform the task of Iranian license plate recognition. In the proposed method
presented in this paper, the license plate recognition is done in two steps.
The first step is to detect the rectangles of the license plates from the input
image. In the second step, these license plates are cropped from the image and
their characters are recognized. For the first step, 3065 images including
license plates and for the second step, 3364 images including characters of
license plates have been prepared and considered as the desired datasets. In
the first step, license plates are detected using the YOLOv4-tiny model, which
is based on Convolutional Neural Network (CNN). In the next step, the
characters of these license plates are recognized using Convolutional Recurrent
Neural Network (CRNN), and Connectionist Temporal Classification (CTC). In the
second step, there is no need to segment and label the characters separately,
only one string of numbers and letters is enough for the labels.
