When looking for product data for Questionmark's sustainability and health scores, we also consider online sources. While most data online is textual, sometimes they're in images.
This is an experiment to find out how hard it would be to extract the data from the image. A good opportunity for some image processing and recognition.
As a sample dataset, we're looking at some images from the Dutch supermarket webshop Hoogvliet, which has nutritional values in an image.
The initial approach was to do word-based recognition using a couple of custom-trained kNN-networks (see nutrient-ocr-knn), but in the end just using tesseract was more convenient. That's what you're seeing here.
Needs Python 2.5+ with PIL and tesseract 3.04 (or higher).
Since the images are quite low-resolution, the program scales them up three times, does thresholding, and calls tesseract. Common misdetections are fixed.
The following example returns ingredients from an image.
$ ./tesseract.py ../nutrient-ocr-knn/imgstest/VOED665279000.png | grep -v '^$'
Voedingswaarde per 100 Gram
Energie 1050 Kilojoule
Energie 251 Kilocalorie
Vetten 12.3 Gram
Vetzuren, totaal verzadigd 8.2 Gram
Koolhydraten 31.4 Gram
Suikers 30.2 Gram
Eiwitten 3.3 Gram
Zout 0.32 GramThat's looking great, and pretty easy.