DocForensics™: Sub-Pixel Document Authenticity Engine
Standard OCR only reads text. DocForensics™ performs deep computer vision forensics on submitted PDFs, mobile photos, and scanned records to detect cloned templates, digital tampering, font kerning alterations, and signature reuse.
Spotting What Human Eyes Miss in Seconds
When fraudsters fabricate health claims, they reuse existing hospital bill layouts, clone doctor signature stamps, or edit patient registration numbers using image editors. DocForensics™ flags the digital fingerprint instantly.
Patient Name: Sunita Devi | Reg: #MLC-88491
Signature Timestamp: 14-Aug-2026 18:30 (Matches Claim #CLM-2025-48291)
6 Multi-Layer Forensic Safeguards
Every submitted file is scrutinized across both visual geometry and file header byte streams.
1. Error Level Analysis (ELA)
Analyzes compression rate differences across image regions to reveal digital splices and pasted figures.
2. EXIF & Byte-Level Telemetry
Extracts camera serials, lens focal lengths, and GPS tags from mobile hospital photos to verify physical hospital location.
3. Signature Hash Matching
Maintains an immutable cryptographic hash bank of physician signatures to spot cloned stamps across unconnected hospitals.
4. Barcode & QR Code Verification
Decodes stent and implant box stickers directly to verify manufacturing batch validity against global GS1 registries.
5. PDF Object Structure Parsing
Inspects embedded TrueType font tables to catch replacement of numerals on total bill amount lines.
6. Multi-Language Handwriting OCR
Deciphers handwritten doctor clinical notes across Hindi, Tamil, Telugu, Marathi, Bengali, and Kannada medical dialects.
Protect Your Claims Gate from Synthetic Invoices
Schedule an enterprise demonstration with our document forensics team and test your submitted claim files.