aae57d7a2bb2bfb75dde4f433131ba7bb585a5a3
date/time, price per litre, litres, discount, total) from German fuel receipt photos: grayscale + mild bilateral filter + contrast stretch, Tesseract OCR, then LLM structuring via a small local model (Qwen 2.5, served by Ollama). Each step is independently runnable and inspectable through numbered scripts against a shared tooling container.
Description
Languages
Kotlin
58.8%
Python
19.3%
Shell
17.8%
Dockerfile
2.8%
Makefile
1.3%