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Amazon SageMaker ×

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

License Plate Detection and Recognition

By: Quantiphi Latest Version: 2.0

Automatic Detection & Recognition of Vehicle License Plate from an image using Deep Learning ML Models

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Product Overview

License Plate Recognition is one of the techniques used for vehicle identification purposes. The sole intention of this product is to find the most efficient way to recognize the number plate information from the digital image . This process involves detecting a vehicle, localizing the license plate and then segmenting & recognizing the characters from the license plate. We used the OpenALPR dataset as the test set to evaluate the accuracy of the proposed method. Our system achieved an average accuracy of 89.33%.

Key Data

Version
By	Quantiphi
Categories Computer Vision Text/OCR Object Detection-Image
Type	Model Package
Fulfillment Methods	Amazon SageMaker

Highlights

Vehicle Detection Model: Trained on Pascal VOC 2007-2012 dataset & accepts images of any size, resizes them to 416x416. Uses YOLOv2 Object Detection Network as a black box, merges the outputs related to vehicles (cars & buses) & ignores other classes. License Plate Detection Model: The images selected for training this Custom CNN for license plate detection included mostly European & American and some Brazilian and Taiwanese license plates. The dataset was created with 200 annotated images from the Cars, SSIG & AOLP dataset. Also about 4000 augmented images were used.
License Plate OCR: The character segmentation and recognition over the rectified license plate is performed using a modified YOLO network. The training dataset was considerably enlarged in this work by using synthetic and augmented data to cope with License Plate characteristics of different regions around the world (Europe, United States and Brazil). The use of synthetic data helped to greatly improve the network generalization, so that the exact same network performs well for License Plates of different regions around the world.
We evaluated the system in terms of the percentage of correctly recognized license plates, considered correct if all characters were correctly recognized. The inference time is highly dependent on the number of vehicles detected in a single image. The avg response time for a single image single vehicle inference on the compute optimized ml.c5.2xlarge instance with 8vCPUs & 16GB Memory is approximately 3.25 secs. Our models are currently configured to use only CPU for inference & our future versions will have GPU inference capability enabled to further optimize the latency.

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Usage Information

Fulfillment Methods

Amazon SageMaker

Input

Supported content types: image/jpeg, image/png

This model accepts images in the mime-type specified above.
The image must be at least 416x416. Model resizes the image to 416x416 before performing the inference.

Output

Content type: text/plain For this image, the ML Model returned following output. Sample output: KL40L5577

Invoking Endpoint

For a single image file: Create an endpoint and provide the source image file in the data/input/real-time directory. The output is obtained in the form of a text file output.txt in the data/output/real-time directory.

Batch Inference

For multiple image files: Batch transform can be used by providing the image files in the data/input/batch directory. The output is obtained in the form of a text file output.txt in the data/output/batch directory which contains the recognized license plate's text.

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This product is offered for free. If there are any questions, please contact us for further clarifications.

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