Description

In a recent project, I tackled the challenge of license plate detection and recognition, a complex problem at the intersection of computer vision and machine learning. The initial phase involved meticulous image collection, where I curated a dataset exclusively composed of images containing license plates. This required not just selecting relevant images but also accurately labeling them to pinpoint the exact location of the plates within each image. Such detailed labeling was crucial as it set the foundation for the subsequent machine learning models.

Building on this well-prepared dataset, I developed a two-stage machine learning pipeline. The first model was trained to predict the location of the license plates with precision, employing state-of-the-art object detection algorithms. Once the plates were localized, a second model took over to decipher the alphanumeric characters on the plates. This optical character recognition phase was critical in transforming visual data into actionable information. Following rigorous training and validation, the models achieved a remarkable level of accuracy. The culmination of this project was the deployment of the entire algorithm via an API, making it accessible for integration into applications, thereby offering a scalable solution for real-time license plate detection and recognition.