Intelligent Identity Document Processing
An AI-assisted verification workflow that extracts identity information, checks document quality, and routes uncertain submissions for human review.
About the Project
OnboardLens AI is designed to help onboarding teams process identity documents more consistently.
The platform captures a document, extracts important fields using optical character recognition, and applies field-level validation rules. Low-confidence or incomplete submissions are transferred to a human reviewer rather than being automatically approved by the system.
The Problem & Objectives
Manual document processing can delay onboarding and introduce transcription errors. Image quality, inconsistent document layouts, and missing information make complete automation unreliable.
The workflow needed to accelerate extraction while keeping final verification decisions under human control.
Scope Limitation
OnboardLens AI is a document helper system. It does not serve as a complete KYC/AML compliance engine unless connected to official verification registries.
Identified Challenges
- [1]
Inconsistent Image Quality
Uploaded document photos being blurred, cropped, rotated, or taken under extremely poor lighting conditions.
- [2]
Structured Field Extraction
Converting unstructured text lines into reliable, parsed names, expiration dates, and identification numbers.
- [3]
Confidence & Validation Thresholds
Identifying low-confidence outputs to prevent auto-approving transcription errors.
- [4]
Manual Review Operations
Building a side-by-side verification interface for managers to cross-reference document photos with extracted JSON text.
How the Solution Was Designed
OnboardLens first checks image quality parameters (blur detection, skew angles) and prepares the document for OCR using binarization filters.
Extracted values are normalized and evaluated through field-level validation rules (checking character scopes, date validity, and checksums).
Each field receives an individual confidence score. Complete submissions with high confidence proceed to the next onboarding step, while files with low-confidence scores or validation mismatches are routed to the human reviewer queue.
Key Features Built into OnboardLens
Core image and data extraction modules designed to verify identities safely with strict audit histories.
Quality Check & Preprocessing
Calculates image sharpness and contrast, automatically auto-rotating documents for better readability.
OCR-Based Extraction
Applies computer vision OCR scripts to locate and read specific fields on national IDs and passports.
Field Normalization Rules
Standardizes dates and formats, highlighting mismatches between check digits and IDs.
Dynamic Confidence Scoring
Scores character recognition confidence per field, flagging items that fall below safety limits.
Manual Review Queues
Routes low-confidence or conflicting files to human dashboards, pausing automatic pipeline advancement.
Verification Audit History
Logs editor corrections, reviewer logins, confidence score triggers, and validation decisions.
Verification Gate Flow
Every submission passes through the same quality gate before it's ever approved.
Upload
The document image is captured or uploaded.
Quality check
Blur detection and skew angle checks assess whether the image is usable.
OCR extraction
Fields are located and read from the document.
Confidence scoring
Each extracted field receives its own confidence score.
High confidence → proceeds
Complete, high-confidence submissions continue to the next onboarding step automatically.
Low confidence → human review
Uncertain or mismatched fields are routed to a reviewer queue instead of being auto-approved.
Technology Infrastructure
The CV libraries, OCR models, and databases powering the OnboardLens platform.
Computer Vision
Backend API
Data Store & Queue
Operations Web Console
Infrastructure
Need intelligent document processing built for your workflows?
Discuss your document structures, OCR requirements, and review queue criteria with our engineering team.