Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.

EasyOCR is a free developer-friendly OCR "Optical Character Recognition" that supports 80 languages including Latin, Chinese, Arabic, and Cyrillic. EasyOCR is written in the Python programming language. It can be installed as a Python package, and integrates well with other Python Frameworks like Django, Flask, and others. You can test the demo here, as you can upload images in different format and test several languages. It comes with a trainer models that can be used to train for new languages, dozens of example datasets for model training, user-friendly instructions on how to train custom recognition models and more. It also supports vertical text, and PIL images, and more.

OCR (Optical Character Recognition) is a technology that enables the conversion of document types such as scanned paper documents, PDF files or pictures taken with a digital camera into editable and searchable data. OCR creates words from letters and sentences from words by selecting and separating letters from images. If you don't have any prior knowledge, I can recommend it. This is a slightly polished and packaged version of the Keras CRNN implementation and the published CRAFT text detection model. It provides a high level API for training a text detection and OCR pipeline.

Reading text in natural images has attracted increasing attention in the computer vision community due to its numerous practical applications in document analysis, scene understanding, robot navigation, and image retrieval. Although previous works have made significant progress in both text detection and text recognition, it is still challenging due to the large variance of text patterns and highly complicated background. The most common way in scene text reading is to divide it into text detection and text recognition, which are handled as two separate tasks. Deep learning based approaches become dominate in both parts. Text Detection: Text Detection is a technique where image will be given to the model and the textual region is detected by plotting a bounding box around it.

Ready-to-use OCR with 70 languages supported including Chinese, Japanese, Korean and Thai. We are currently supporting 70 languages. See list of supported languages. Note 1: for Windows, please install torch and torchvision first by following the official instruction here https://pytorch.org. On pytorch website, be sure to select the right CUDA version you have.