VeriScanxSIH 2026
Smart India Hackathon 2026 — Border & Immigration Tech

Verify every travel document in seconds, not minutes.

VeriScanx is a computer-vision pipeline that inspects passports and ID documents for tampering, cross-checks passenger details against live government and international records, catches duplicate or blacklisted identities, and hands the counter officer one clear risk score — before the traveler has finished stepping up to the desk.

<8sTarget verify time
6Checks per document
24/7Kiosk & counter ready
<8sTarget verify time
6Checks per scan
DOC/SCAN/0417-ALIVE
P<NRDVERMA<<ANIKA<<<<<<<<<
402183756234NRD9006014F3001128<<12
Risk score 6 · Clear
Exhibit 01 — The problem

Manual document checks don't scale, and forged documents don't announce themselves.

Immigration counters and airport kiosks still lean on a trained eye and a UV lamp. That's slow at peak hours, inconsistent across shifts, and easy to beat with a well-made forgery, a photo-swapped passport, or a document that's already on a blacklist under a different alias.

Throughput bottleneck

Peak-hour queues grow because each document needs a manual look, even when 90%+ are genuine.

Inconsistent scrutiny

Fatigue, shift changes, and lighting mean the same forged page can pass one officer and not another.

Siloed watchlists

National blacklists, Interpol-style alerts, and prior-scan history rarely get cross-checked in real time.

Exhibit 02 — How it works

One scan, six checks, one decision.

Every document that comes through the kiosk or counter scanner moves through the same pipeline, so the outcome is repeatable regardless of who's on shift.

01

Capture & upload

Kiosk camera, counter scanner, or mobile app ingests the document image and, where available, the NFC chip data.

02

OCR & MRZ parsing

Extracts the visual fields and machine-readable zone, then validates the MRZ checksum digits.

03

Image forensics

Error-level analysis and print-pattern checks surface splicing, re-prints, and photo substitution.

04

Database cross-check

Queries issuing-authority records, the national blacklist, and international watchlists in real time.

05

Identity resolution

Face-matches the document photo and searches prior scans for duplicate or multiple identities.

06

Risk scoring

A weighted, explainable 0–100 score routes the traveler to clear, secondary review, or escalation.

Exhibit 03 — What it catches

Built around the ways travel documents actually get faked.

Tamper & forgery detection

Error-level analysis and font/kerning consistency flag edited regions and photo swaps.

MRZ & field validation

Checksum digits and cross-field consistency (name, DOB, document number) are verified automatically.

Blacklist & watchlist matching

Real-time lookup against national blacklists and international alert lists on every scan.

Multiple-identity detection

Searches prior scans for the same face or biographic details filed under a different name.

Real-time, explainable risk score

A weighted 0–100 score with a visible breakdown, not a black-box yes/no.

Officer dashboard & audit trail

Every scan, decision, and override is logged for review and analytics.

Exhibit 04 — Architecture

Five layers, one pipeline.

01 · Capture
Airport kiosk
Counter scanner
Mobile officer app
↓ verification request
02 · API gateway
Verification API
Auth & rate limiting
↓ dispatch
03 · Processing core
OCR / MRZ engine
CV forensics engine
Face-match engine
↓ lookups
04 · Data layer
National ID registry
Blacklist DB
Int'l watchlist
Prior-scans store
↓ aggregate
05 · Decision
Risk engine
Officer dashboard
Alerts & audit log
Proposed stack
ReactPython / FastAPIOpenCVTesseract OCRONNX face-match modelPostgreSQLRedis queueGovt DB connector (mock in demo)
Exhibit 05 — Projected impact

What we're targeting for the pilot.

These are design targets for the prototype, not measured production figures — they're what the pipeline above is architected to hit.

70%↓
Average counter processing time vs. fully manual checks
6
Independent checks run on every single document
~5–10%
Share of scans expected to route to manual review
100%
Of scans logged with a full, auditable decision trail
Exhibit 06 — Team

Built for SIH 2026.

TL
[Team Lead]Team Lead
CV
[Your Name]Computer Vision / ML
BE
[Your Name]Backend Engineer
FE
[Your Name]Frontend Engineer
UX
[Your Name]UI / UX Design
DR
[Your Name]Research & Docs
Ready when you are

See it catch a forgery, live in your browser.

No install, no account — upload a document or try a fictional specimen and watch the real tamper-detection pass run in seconds.