KDG INPUT
Correct Knowledge Starts with Correct Reading
No matter how advanced the AI, wrong input means wrong answers
Japanese industrial documents contain complex layouts where meaning exists only within tables and figures. KDG INPUT uses proprietary analysis techniques to digitize these documents — which general-purpose reading methods can struggle with — preserving their structure and feeding them accurately into KDG's knowledge base.
Feature 01
Computer Vision for Complex Tables
Analyzes complex tables with merged cells, nested structures, and handwritten annotations at the pixel level. Detects rules, identifies intersections, and infers cell structure to generate R1C1-labeled guide images that convey structural information to LLMs for accurate digitization.
- 1Pixel-level rule detection accurately identifies merged cells and nested structures
- 2R1C1-labeled guide images enable LLMs to extract cell content accurately
- 3Multi-layer analysis handles handwritten notes, stamps, and in-drawing text
Feature 02
Block Extraction & Semantic Grouping
Automatically extracts content blocks from PDFs and groups semantically related blocks. Recognizes headings, body text, tables, and figures while preserving the original hierarchy (section → subsection → paragraph) as structured Markdown with embedded block IDs for full traceability.
- 1Recognizes headings, body, tables, and figures while preserving document hierarchy
- 2Embedded block IDs enable tracing from extracted text back to source PDF
- 3Semantic grouping automatically organizes content into meaningful units
Feature 03
Requirements Extraction Pipeline
Extracts source-faithful atomic sentences and domain terminology from digitized documents, converting them into specification-grade requirement statements with automatic domain classification, category judgment, and confidence scoring.
- 1Source-faithful atomic sentence extraction prevents information loss
- 2Automatic domain, category, and confidence assignment visualizes requirement quality
- 3Auto-generates requirement specifications with cover page, TOC, and glossary
Feature 04
Version Diff Detection
Automatically detects differences between versions of technical specifications at character-position accuracy. Uses intelligent lookahead algorithms for group matching combined with LLM semantic similarity evaluation to generate reports explaining not just what changed but why it matters.
- 1Lookahead algorithm accurately handles additions and deletions in group matching
- 2Changes classified by severity: critical, addition, deletion, minor
- 3Impact-annotated reports help reduce review effort
Feature 05
Multi-Layer Accuracy Verification
KDG INPUT places strong emphasis on reading accuracy. Multiple verification layers — LLM confidence scores, character-position-based source linking, structural validation, and fallback mechanisms — allow all extracted information to be traced back to its source.
- 1LLM confidence scores (0.0–1.0) quantitatively evaluate extraction accuracy
- 2Character-position-based linking enables instant reference to source locations
- 3Structural validation and fallback mechanisms provide two layers of quality checks
Start with Correct Digitization
Take the first step toward building your organization's knowledge base by accurately digitizing complex Japanese technical documents. See KDG INPUT in action.