Issue in Detail:
I'm experiencing difficulties in achieving high accuracy when using Google Document AI for parsing invoices and expenses. I am currently working with custom datasets that include approximately 1,500 images/documents per parser (Invoice Parser and Expense Parser). My goal is to reliably extract key data from these documents, but due to the diverse layouts and formats in my dataset, the F1 scores remain low, ranging between 0.75 and 0.85. This performance issue becomes even more apparent when testing with new images or documents; in these cases, predictions are frequently inaccurate or sometimes miss important fields.
What I've Already Tried:
- I have invested significant time in annotating around 1,500 images/documents for each parser, following recommended steps to ensure consistent labeling.
- I also followed the official annotation guidelines as recommended here to improve data quality and maintain annotation standards.
- The dataset I’m using contains a mix of scanned documents, soft copies, regular images, and photo images, aiming to improve model robustness. However, despite these efforts, the F1 scores are not improving beyond 0.75-0.85, and model predictions on new document layouts remain inconsistent.
I would appreciate any insights or recommendations for improving model accuracy, particularly with datasets that contain various layouts. Thank you!
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