License Plate Detection and OCR Web App with TensorFlow and Flask

Computer vision and text extraction are transforming industries, but bridging the gap between a trained model and a working web application can be challenging. This course guides you through the entire process of building an intelligent license plate recognition system from scratch. You will transition from understanding basic image processing concepts to constructing a complete deep learning pipeline. Through detailed written explanations and structured code exercises, you will learn how to prepare image data, train an object detection model to locate license plates, perform Optical Character Recognition (OCR) to read the text, and bundle everything into a clean web interface. What you'll learn: - Understand the fundamentals of image preprocessing and annotation for computer vision. - Train a deep learning object detection model using TensorFlow to locate regions of interest. - Apply OCR techniques to accurately extract text from detected license plates. - Build a web application backend using Flask to handle image uploads and model inference. - Design a responsive user interface using HTML and Bootstrap to display detection results. - Implement modern API design principles to cleanly connect your deep learning pipeline with the frontend. The journey begins with core computer vision definitions and data labeling techniques before moving into model training and evaluation. Finally, you will connect your trained model to a web backend, creating a cohesive, end-to-end application. This course is designed for beginners interested in computer vision and web development. No prior experience with deep learning or web frameworks is required, as we start with foundational concepts. Start reading today to build your first intelligent web application.