AI-Powered Document Processing Pipeline

Successfully implemented the BERT-based entity extraction module. Initial tests show 92% accuracy on invoice data, but struggling with handwritten notes. Need to explore vision transformers for the handwriting component.

Benchmarked three different OCR approaches: Tesseract (baseline), AWS Textract, and our custom CNN model. Custom CNN achieved 89% accuracy on standard forms but only 67% on complex multi-column layouts. This confirms the need for a more sophisticated architecture.

feat: implement multi-head attention for document layout analysis Added transformer encoder with 8 attention heads for spatial relationship modeling. Early results show promising improvements on table detection.

After reviewing the literature on document understanding, we hypothesize that combining visual features (CNN backbone) with textual features (BERT embeddings) in a unified model will outperform single-modality approaches for our use case of mixed-format documents.

The multi-modal fusion approach achieved 94.2% accuracy on our test set, validating our hypothesis. However, inference time increased by 40%. Next step: explore model distillation to reduce latency without significant accuracy loss.

fix: resolve memory leak in batch processing pipeline Fixed tensor accumulation issue causing OOM errors on large document batches. Added proper gradient detachment and implemented chunked processing.

Based on our experiments, we conclude that the transformer-based approach is viable for production. Key findings: (1) Multi-modal fusion improves accuracy by 12% over single-modality, (2) Knowledge distillation can recover 90% of accuracy at 3x speedup, (3) Edge cases with handwritten annotations still need specialized handling.

Explored using GPT-4 Vision API for complex document understanding as a potential benchmark. Results: 96% accuracy but $0.03 per page cost and 2-3 second latency makes it impractical for high-volume processing. Our custom model remains the better choice for production.