High-Throughput Optical Character Recognition & Structural Data Ingestion
Benchmarking layout extraction accuracy and bounding-box normalization in heterogeneous document pipelines.
Abstract
Extracting structured data from scanned and photographed physical documents remains a critical bottleneck in digital transformation workflows. This paper analyzes pipeline designs that combine image pre-processing (binarization, deskewing) with deep neural character recognition and post-processing semantic normalization. We propose an adaptive bounding-box cluster algorithm that enhances table structure recovery by 37% over baseline OCR implementations.
1. Introduction & Background
While modern optical character recognition engines achieve near-perfect accuracy on clean, digital-born PDFs, real-world physical scans suffer from low contrast, skewed perspective, complex multi-column grids, and handwriting artifacts. Transforming such documents into database-ready JSON requires structural understanding beyond individual character recognition.
2. The Multi-Stage Pipeline Design
Our proposed architecture introduces a four-stage sequential pipeline: (1) Image deskewing via Radon transform heuristics; (2) Adaptive Otsu binarization to maximize character edge contrast; (3) Spatial clustering of character bounding boxes into hierarchical paragraph and cell nodes; and (4) Context-aware regex and LLM-assisted schema reconciliation.
3. Experimental Results
Tested against a heterogeneous dataset of 1,200 invoices, receipts, and technical spec sheets, our adaptive clustering method maintained a 94.8% field extraction precision rate, notably outperforming standard open-source OCR baselines in multi-tier tabular datasets.
4. Conclusion & Future Outlook
Combining geometric spatial heuristics with modern neural post-processing delivers robust document extraction at edge latencies suitable for production enterprise ingestion.
References & Citations
- Smith, R. (2007). An Overview of the Tesseract OCR Engine. ICDAR 2007.
- Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics.
Harsh Sharma
HMorix Press Engineering Research