Background to the issue
When dealing with unstructured data such as scanned and photographed documents, traditional OCR often results in misplaced forms and misrecognition of handwriting, etc. Rowfill's Hybrid Recognition Engine can be targeted to solve this problem.
Accuracy Improvement Program
- Multimodal processing:
- Enable high-precision OCR mode for scans (needs to be set in the environment variable)
OCR_QUALITY=high) - Automatic perspective correction of cell phone photo documents (requires checking the "Intelligent Preprocessing" option)
- Enable high-precision OCR mode for scans (needs to be set in the environment variable)
- Calibration mechanism:
- Secondary checks via local LLM (e.g. checking extracted amount data with Mistral model)
- Set confidence thresholds (data below 90% are automatically yellow labeled for alerts)
Special Scene Handling
Recommendations for complex scenarios:
- Handwriting Recognition: Prioritize cloud version (Alpha version integrates enhanced AI models)
- Cross-page forms: Enable the "Form Continuation Detection" parameter in the workflow
Fault tolerance program
When identifying anomalies: 1) Analyze the specific error code through logs 2) Adjust the document scanning DPI to 300 or above 3) Contact the community for model tuning parameters
This answer comes from the articleRowfill: Batch Extraction of Structured Information from Documents and Automated AnalysisThe
