![]() ![]() With that, we are finally ready with our App. In the final module, we will learn to create a web app project using FLASK Python. Now, we will put it all together and build a Pipeline Deep Learning model. In this module, we will extract text from images. ![]() Once we have done with the Object Detection model training process, then using this model we will crop the image which contains the license plate which is also called the region of interest (ROI), and pass the ROI to Optical Character Recognition API Tesseract in Python ( PyTesseract). Then after labeling the image we will work on data preprocessing, build and train a deep learning object detection model (Inception Resnet V2) in TensorFlow 2. Then we have to label images for object detection of License Plate or Number Plate using Image Annotation Tool which is open-source software developed in python GUI. But this article limits to only the first three modules. Labeling, Training, Save Model, OCR and Pipeline, and RESTful API. In the above architecture, there are six modules. Figure-1: Number Plate Recognition Project Architecture
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