Odometer reading automation using Computer Vision | Inspektlabs

OCR Systems

Optical Character Recognition or OCR is the challenge of extracting text from images and documents and has been the focus of much research for a long time.

There are various open-source software tools like Python-Tesseract and other tools like Amazon’s Rekognition that can “read” the text embedded in an image. Google has its own Tesseract Project as well, which was originally developed by Hewlett-Packard in the 1980s.

So, whether it’s printed documents or the Bus-Stop signboards or handwriting in general, a well-trained OCR system should be able to extract the written content from it and keep a digital copy.

This technology is increasingly being used for rapid digitization across various industries, including book scanning (the Gutenberg Project, for instance).

OCR on Seven Segment Displays

OCR systems were quite complex and expensive until a few decades ago, but with steady advancements in computer vision and deep learning, AI OCR for vehicle inspection has made it possible to automate vehicle inspections across the automotive industry.

Having said that, seven-segment displays remain a sector that hasn’t been explored in its entirety in terms of character recognition. Medical equipment, digital watches, and car odometers, for instance, show the data using seven-segment displays, and extracting the text from it remains a niche sector.

But if we were to go ahead with it, we would have to classify every digit separately and perform detections on images using the size and position of the bounding boxes. Moreover, object detection requires a huge amount of data to perform decently. But there’s a way out.

Data Generation

Since it may not be practical to source thousands of odometer images, we can always swap the digits in a given image to create new training data synthetically. This will create various permutations of a single image.

We can also use XMLTree and OpenCV to develop a data generation pipeline. This will place random numbers over previously labelled positions, thereby generating a whole new image data point. We can also add noise to the image to avoid edge-detection training.

The Model

We can implement our model in Python 3.7 since there exists a diverse set of libraries for computer vision and machine learning. OpenCV will obviously be used for feature extraction along with NumPy and XML Element Tree.

For detecting digits, we can mainly depend upon YOLO (You Only Look Once), which is an object detection paradigm. It is a strategy or an algorithm employed, just like R-CNN and Single Shot Detector, to detect objects faster than its counterparts.

YOLOv3 is the latest and the best one out of the YOLO family. We can also improve localization with images that are generated with label reshuffling. A good model should be able to localize the digits and classify them thereafter by ensembling our localization model with a convolutional neural network.

Similar computer vision techniques are also used to detect and recognize VINs accurately, enabling automated verification as part of digital vehicle inspection workflows and helping reduce fraud. Learn how Inspektlabs is tackling VIN fraud.

Final Thoughts

In this article, we talked about what OCR is and how it can possibly be used to extract text from seven-segment displays like car odometers.

For your own usage, one can obviously use the existing tools, including the commercially available Bixby Vision and Google Lens. Again, we can use existing tools like Tesseract or Attention OCR, but training our own model that’s designed specifically to detect digits in seven-segment displays is bound to perform better and support a wide range of automotive inspection technology use cases.

Frequently Asked Questions

1. Can AI automatically read odometer readings from vehicle photos?
Yes. AI-powered OCR can read odometer readings directly from vehicle photos and convert the displayed number into structured digital data. Inspektlabs uses a computer vision model specifically designed for vehicle odometers, including seven-segment displays, rather than relying only on general-purpose OCR tools. 

2. How accurate is AI-powered odometer reading from vehicle images?
Accuracy depends on factors such as image quality, display visibility, and how clearly the digits are captured. A model trained specifically for odometer displays can perform better than generic OCR because it is designed around the layout and characteristics of these displays. Inspektlabs uses specialized training and synthetic data generation to improve recognition across different odometer images.

3. Can odometer reading be automated as part of a digital vehicle inspection?
Yes. Odometer OCR can be included as one step in a digital vehicle inspection workflow. The captured image can be processed automatically to extract the mileage and associate it with the vehicle inspection record, reducing the need for an inspector to enter the reading manually.

4. Can OCR read digital and seven-segment car odometers?
Yes. Seven-segment displays are a specific OCR challenge because the digits are formed from individual segments rather than conventional characters. Inspektlabs has built a dedicated seven-segment OCR approach that detects and classifies individual digits before reconstructing the complete odometer reading.

5. Can automated odometer reading reduce manual data entry during vehicle inspections?
Yes. Instead of asking an inspector or customer to type the mileage, AI can extract the odometer reading directly from the inspection image. This reduces repetitive data entry and helps keep the recorded mileage linked to the correct vehicle inspection.

6. What types of vehicle inspection workflows can use automated odometer reading?
Automated odometer reading can be used in insurance inspections, fleet checks, rental vehicle handovers, used-car assessments, auctions, and other workflows where mileage is part of the vehicle record. Combined with AI vehicle inspection software, the extracted reading can become part of a broader digital inspection report alongside vehicle damage and identification data.