
OCR and AI-based document processing systems can struggle with:
- Poor-quality scans (blurry, skewed, low resolution)
- Handwritten or stylized fonts.
- Complex layouts (tables, multi-column text).
- Domain-specific terminology (legal, medical, and technical documents).
HITL bridges the gap by having humans review, correct, or validate uncertain outputs.
Human-in-the-loop verification helps to deal with issues by:
- Pre-Processing – Humans adjust alignment, image quality, and segmentation.
- OCR Correction – AI flags low-confidence text (e.g., “5” vs. “S”), humans verify.
- Data Validation – Extracted fields (dates, amounts) are cross-checked manually.
- Layout Recovery – Tables, headers, and formatting are corrected post-OCR.
- AI Training – Human feedback improves models over time.

1. Rule-Based Systems Handle Structured Data Well.

OCR stands for Optical Character Recognition and is the technology that allows software to interpret text on scanned images. When this technology is applied to automating business data entry processes it’s referred to as OCR Data Capture.
Any organization that collects data from paper documents, or electronic files like PDF and Office documents, can get a very high return on investment by automating the data entry with OCR data capture software.
