How I Replicated an $86 Million Project in 57 Lines of Code

Exploring the Capabilities and Potential of Open-Source License Plate Recognition

In 2019, Victoria Police in Australia embarked on an ambitious project to roll out Automatic License Plate Recognition (ALPR) technology across their fleet of 220 highway patrol vehicles. The quoted cost was a staggering $86 million – or roughly $390,000 per vehicle. As a software engineer and technology enthusiast, I was intrigued by the figure. Could a similar system be developed using open-source software libraries and off-the-shelf hardware components, at a small fraction of the cost? I set out to find the answer.

Understanding ALPR Systems

At its core, an ALPR system consists of three main components:

1. Cameras to capture images or video footage of license plates. These can be specialized units with integrated illumination and processing capabilities, or general-purpose cameras.

2. Computer vision software to detect and extract license plate regions from the captured images. This is a complex task involving a variety of image processing techniques.

3. Optical Character Recognition (OCR) engine to convert the extracted license plate images into machine-readable text. Modern OCR systems leverage deep learning models trained on vast datasets of license plates.

The first step in developing my low-cost ALPR system was to understand the math and algorithms behind each of these components.

License Plate Detection

Identifying potential license plate regions within an image is a classic computer vision problem. The most common approach consists of the following steps:

1. Pre-processing the input image to normalize lighting and remove noise. This can involve techniques like contrast enhancement, bilateral filtering, and adaptive thresholding.

2. Edge detection to find regions with high contrast gradients that could correspond to plate boundaries. Popular algorithms include Sobel, Canny and Laplacian operators.

3. Binarization to convert the grayscale edge map into a black and white image suitable for further analysis. Otsu‘s method and adaptive thresholding are commonly used.

4. Connected component analysis to identify and label distinct regions in the binary image. Algorithms like flood filling and two-pass labeling are used to group adjacent pixels.

5. Geometrical filtering to select regions that match the expected size, aspect ratio and shape of a license plate. This step eliminates false positives like road signs and car grills.

Here‘s a visualization of the license plate detection pipeline:

Once the candidate license plate regions are extracted, they are passed to the OCR engine for text recognition.

Optical Character Recognition

OCR technology has evolved significantly over the past few decades. Traditional approaches relied on hand-crafted feature extraction and template matching against a pre-defined set of character images. However, the accuracy of these methods was limited by the variability of real-world license plates in terms of fonts, colors, and image quality.

Modern OCR engines leverage deep learning, specifically Convolutional Neural Networks (CNNs), to learn hierarchical feature representations directly from training data. A CNN consists of multiple layers that apply convolution operations to the input image, learning to detect increasingly abstract patterns. The final layers of the network output a probability distribution over the possible characters at each position in the sequence.

Training a CNN-based OCR model requires a large dataset of labeled license plate images. The model parameters (weights and biases) are optimized through backpropagation to minimize a loss function that measures the difference between predicted and ground truth character sequences. Popular architecture choices for OCR include multi-layer perceptrons, Long Short-Term Memory (LSTM) networks, and Connectionist Temporal Classification (CTC) loss.

The diagram below shows a simplified architecture of a CNN-based OCR model:

By leveraging transfer learning from models pre-trained on large generic datasets like ImageNet, even a small amount of domain-specific license plate data can yield significant accuracy improvements.

Open-Source ALPR Libraries

Fortunately, implementing an ALPR system from scratch is not necessary for most applications. There are several mature open-source libraries that provide complete pipelines for license plate detection and recognition. Two of the most popular are:

1. OpenALPR: An open-source ALPR library written in C++ with bindings for various programming languages. It uses a combination of traditional computer vision techniques for license plate detection and a CNN-based OCR engine for character recognition.

2. PlateRecognizer: A cloud-based ALPR API with a free open-source SDK for integrating license plate recognition into applications. It offers pre-trained models for over 100 countries and can be self-hosted for on-premise deployment.

For my prototype system, I chose OpenALPR due to its extensive documentation and active community support. Here‘s a code snippet demonstrating how to perform license plate recognition with OpenALPR in Python:

from openalpr import Alpr

alpr = Alpr("us", "path/to/openalpr.conf", "path/to/runtime_data")

if not alpr.is_loaded():
    print("Error loading OpenALPR")
    sys.exit(1)

results = alpr.recognize_file("path/to/image.jpg")

i = 0
for plate in results[‘results‘]:
    i += 1
    print("Plate #%d" % i)
    print("   %12s %12s" % ("Plate", "Confidence"))
    for candidate in plate[‘candidates‘]:
        prefix = "-"
        if candidate[‘matches_template‘]:
            prefix = "*"

        print("  %s %12s%12f" % (prefix, candidate[‘plate‘], candidate[‘confidence‘]))

alpr.unload()

With just a few lines of code, we can extract license plate text from an input image along with confidence scores for each character. OpenALPR also provides pre-trained models for various regions that can be further optimized on a custom dataset.

Training a Custom OCR Model

While OpenALPR‘s default models provide a good starting point, achieving the best performance often requires fine-tuning on a dataset representative of the specific deployment scenario. This is especially true for regions with unique license plate designs or formats.

To train a custom OCR model, the first step is to gather a large dataset of labeled license plate images. This can be a time-consuming and expensive process, as it requires manually annotating each character bounding box. However, there are a few public datasets available for research purposes, such as:

• AOLP (Application-Oriented License Plate): A dataset of 2049 images of Taiwan license plates captured in various real-world scenarios.
• SSIG-SegPlate: A dataset of 2000 images of Brazilian license plates with character-level bounding box annotations.
• UFPR-ALPR: A dataset of 4500 images of Brazilian license plates captured by a static camera under different lighting and weather conditions.

For my prototype system, I used a combination of public datasets and images from dashcam footage to create a training dataset of around 10,000 license plate images. I then used the OpenALPR training toolkit to fine-tune the default OCR model on this dataset.

The training process involves the following steps:

1. Annotating the dataset: Each license plate image needs to be labeled with the true character sequence and bounding boxes for individual characters. This can be done using tools like LabelImg or VGG Image Annotator.

2. Preparing the training data: The annotated images are split into training and validation sets, typically in an 80:20 ratio. The data is then converted into a format suitable for input to the OCR model, such as TensorFlow‘s TFRecord.

3. Configuring the model architecture: The choice of OCR model architecture depends on factors like the character set size, image resolution, and computational constraints. For my prototype, I used a CNN-LSTM architecture with CTC loss, which has shown good performance on license plate recognition tasks.

4. Training the model: The model is trained on the prepared dataset using an optimization algorithm like Adam or RMSprop. Hyperparameters such as learning rate, batch size, and regularization strength are tuned to achieve the best performance on the validation set.

5. Evaluating the model: The trained model is evaluated on a held-out test set to measure its accuracy and generalization performance. Metrics like character error rate (CER) and sequence error rate (SER) are commonly used for OCR tasks.

After fine-tuning the OCR model on my custom dataset, I observed a significant improvement in recognition accuracy, especially for challenging cases like partial occlusion and extreme viewpoints.

Deployment and Scaling

With the license plate detection and recognition components in place, the final step was to integrate them into a complete ALPR system that could be deployed at scale. For my prototype, I used a Raspberry Pi 4 single-board computer with a USB webcam as the image capture device.

The Raspberry Pi runs a Python script that continuously captures frames from the webcam, processes them with the OpenALPR library, and sends the recognized license plate text to a central server for further analysis. The server component is responsible for tasks like querying vehicle registration databases, generating alerts for plates of interest, and storing metadata for later retrieval.

To make the system more robust and scalable, I implemented several additional features:

• Parallel processing: The license plate detection and recognition steps are computationally expensive, especially when processing high-resolution video footage. To improve throughput, I used Python‘s multiprocessing library to parallelize the OpenALPR pipeline across multiple CPU cores.

• Caching and buffering: To reduce latency and network traffic, I implemented a caching mechanism that stores recently recognized plates and their metadata on the edge device. The device only sends updates to the server when a new unique plate is detected or a certain time interval has elapsed.

• Redundancy and failover: In a production ALPR deployment, it‘s critical to ensure high availability and minimize downtime. I designed my system with redundant edge devices and server instances that can take over in case of failures. I also implemented health monitoring and log aggregation to quickly detect and troubleshoot issues.

• Security and privacy: ALPR systems collect sensitive personal data that needs to be protected from unauthorized access and misuse. I implemented encryption for data in transit and at rest, access controls based on user roles, and audit logging to track all system activities. I also designed the system to automatically delete data after a configurable retention period to comply with privacy regulations.

Here‘s a high-level architecture diagram of my prototype ALPR system:

By leveraging open-source software, commodity hardware, and cloud services, I was able to build a scalable and cost-effective ALPR system that could be easily adapted for various use cases.

Conclusion

This project demonstrated that it is indeed possible to replicate the core functionality of an $86 million ALPR system with just a few dozen lines of code and off-the-shelf components. By leveraging the power of open-source libraries like OpenALPR and TensorFlow, developers can build custom ALPR solutions that are tailored to their specific needs and budgets.

Of course, a production-grade ALPR system for law enforcement or toll collection would require additional features and engineering efforts beyond what was covered in this prototype. However, the core building blocks – license plate detection, OCR, and data processing – remain the same regardless of the scale.

As the accuracy and performance of open-source ALPR tools continue to improve, we can expect to see more and more organizations adopting this technology for a wide range of applications. From parking management and access control to traffic analysis and public safety, the possibilities are endless.

At the same time, the increasing accessibility of ALPR raises important questions around privacy, security, and responsible use of the technology. As developers and engineers, we have a responsibility to design systems that are not only accurate and efficient but also transparent, accountable, and respectful of individual rights.

By striking the right balance between innovation and ethics, we can harness the power of ALPR to create a safer and more connected world for everyone.