Build a system along with a mobile app that accepts images containing up to 10 geographical addresses in various formats, including handwritten notes, printed shipping labels, or mixed document types. Using OCR libraries, the system must accurately extract and recognize these addresses while handling common challenges like poor image quality, different handwriting styles, and varying text orientations.
Once addresses are extracted, the system should validate and parse them to ensure they represent legitimate geographical locations. This involves implementing error handling for OCR inaccuracies and providing confidence scores for each detected address. The system must support multiple image formats, including JPG and PNG, while accommodating different image orientations and lighting conditions.
The route optimization component should obtain the delivery person's current GPS coordinates and geocode all extracted addresses to precise map coordinates. The core challenge involves calculating the most efficient route that visits all locations while minimizing total travel time and/or distance. This requires solving a variant of the Traveling Salesman Problem while considering real-time traffic conditions, road restrictions, and delivery constraints (if possible).
The system should integrate multiple APIs for OCR processing, geocoding services, and mapping functionality while gracefully handling failures such as unrecognizable addresses or unreachable locations. Technical implementation requires image preprocessing techniques to enhance OCR accuracy, text parsing algorithms to distinguish addresses from other document content, and robust optimization algorithms for route planning. The final system should provide clear navigation instructions and handle edge cases that commonly occur in real-world delivery scenarios.