In today’s digital-first world, organizations process millions of documents every day. Whether it’s legal paperwork, financial forms, or HR onboarding documents, one thing remains consistent—the need to detect handwritten signatures accurately and automatically.
At IDV Pacific, we set out to solve this challenge with a new generation of AI-powered signature detection. The result? A system that combines state-of-the-art deep learning with real-time performance, tested across tens of thousands of the toughest scenarios.

Detecting signatures isn’t as simple as identifying dark marks on a page. It requires an understanding of shape, style, context, and variation—all of which can be distorted by scanning issues, background noise, or inconsistent layouts.
Traditional OCR-based methods and rule engines struggle under this variability. Our solution uses a deep learning approach that learns from real-world document complexity and scales seamlessly to production volumes.

We’ve developed a high-performance detection engine built on top of a powerful deep learning framework and optimized with NVIDIA acceleration.
The system has been trained on a vast and diverse dataset, enabling it to adapt to different document styles, formats, and noise patterns.

What Makes It Stand Out?

Real-Time Speed – Optimized for edge inference and high-throughput pipelines.

High Accuracy – Tuned to maximize F1 ScoreⓘThe F1 Score is a statistical measure that balances precision and recall to indicate the accuracy of a model’s predictions, ranging from 0 (worst) to 1 (best). and reduce false positives.

Low Latency – Minimal delay even under load, suitable for enterprise-scale automation.

During development, we validated the model extensively using industry-standard benchmarks. Here’s a snapshot of our performance under general conditions:

F1 ScoreⓘThe F1 Score is a statistical measure that balances precision and recall to indicate the accuracy of a model’s predictions, ranging from 0 (worst) to 1 (best). : Peaking at 0.98, ensuring an excellent balance between precision and recall.

Precision: Hitting 100% at optimal thresholds (>93% confidence).

Recall: Sustained high levels across diverse datasets.

mAP@0.5ⓘThe mAP@0.5 score measures the average precision of a model’s predictions at an Intersection over Union (IoU) threshold of 0.5, indicating how well predicted objects match ground truth. : 0.991 – indicative of high detection quality across class and position variance.

These aren’t just internal numbers—they reflect how our system behaves in production, where accuracy matters most.

We believe a great model isn’t just judged by its average case performance—but by how it handles the hardest cases.

That’s why we ran our solution through 50,000+ edge test cases—the corner cases where most systems fail. These included:

• Faint or smudged signatures
• Rotated and misaligned documents
• Overlapping stamps or graphical noise
• Uncommon signature placements
• Partially obscured or cropped signatures

The result?

Our model achieved a 98%+ success rate, even on these high-difficulty scenarios.

Why It Matters

In high-stakes environments like legal or financial processing, you can’t afford to miss a signature—or trigger a false alarm. Our edge-case robustness ensures that you maintain accuracy under any condition.

We’re proud of what we’ve built, but we also respect the importance of discretion. While our solution leverages next-gen AI and industry-leading hardware, we don’t expose specifics of the architecture or training pipeline publicly.

What we can say is this:

“Our system is engineered for production reliability and real-world variability—backed by data, tested at scale, and fine-tuned for business-critical deployments.”

Our intelligent signature detection engine is ready to plug into your workflows across multiple industries:

• Banking & Financial Services
  Detect signatures on checks, forms, or scanned contracts.
• Legal Tech & Compliance
  Speed up contract processing, verification, and audits.
• Insurance
  Automate claim validation with signature-based document verification.
• Enterprise Document Automation
  Supercharge your document workflows with smart detection and classification.
• Gaming and Hospitality
  Identify and extract signatures from driver licences and other identity documents to support visitor management processes.

Whether it’s a pristine signature on a clean form or a distorted scribble on a faxed document, our detection system delivers fast, accurate, and consistent results.