Background: Food waste is a common problem in developed economies. In Switzerland, the average household throws away about 30 percent of still edible food. Creating a food plan and buying only food for what you want to cook may reduce the amount of wasted food drastically.
The goal of Ingrid is to extract ingredients from recipes into a checklist in order to simplify the process of getting missing ingredients. The ultimate use case would be an integration of Ingrid to local food delivery services.
Use existing libraries (free or under GPL, Apache, etc.) for optical character recognition and image preprocessing (if not done by OCR). See https://en.wikipedia.org/wiki/Comparison_of_optical_character_recognition_software
Added Tess4J (Java library) to the build path and start doing some text recognition from some pictures taken by my smartphone. Used following configuration: language (eng)
- Traffic bypass information [Input picture] [Output text]
- Recipe for penne [Input picture] [Output text]
- USA strategy paper for Near East and South Asia (1983) [Input picture] [Output text]
![[Input picture]](https://github.com/yaw89/ingrid/blob/master/backend/resources/raw_images/ausbau_nordumfahrung_a1.jpg){kind=link}
![[Input picture]](https://github.com/yaw89/ingrid/blob/master/backend/resources/raw_images/penne_rezept.jpg){kind=link}
The recognition of the strategy paper (8.2 MB, TIFF, English) was remarkably faster compared to the traffic bypass and recipe (~2 MB, JPG, German).
Use OpenCV for preprocessing images before it is fed into text recognition.
Update: Before text recognition, a text detection algorithm should be run over the image. This may lead to better text recognition result.
- Installing OpenCV for Java: http://opencv-java-tutorials.readthedocs.io/en/latest/01-installing-opencv-for-java.html