Home / Case Studies / KYC Verification

/ Case Study · Finance

A KYC Verification System, Automated. Documents Read, Data Verified.

/ The engagement

Computer Vision for KYC, End to End.

A KYC platform built on TensorFlow, OpenCV, PyTorch, NumPy, and Faster R-CNN that extracts and validates the critical fields on identity documents while keeping the data accurate and secure.

TensorFlowFaster R-CNNOpenCVData Validation
Faster R-CNNDetecting document regions
DenoisingCleaner input images
AutomatedExtraction and validation
ConsistentStandardized output text

/ About the client

KYC Verification

The client is a leading Philippines financial services provider that puts technology to work for customer service. Their clientele includes BPOs serving the global market and enterprises with demanding system requirements.

The KYC Verification Platform is a software solution that streamlines and strengthens the KYC process. It automates extracting and validating the critical information on KYC documents while keeping data accurate and secure.

/ The approach

Design, architecture, and features, shipped as one program.

/01Denoise

Cleaner images first

Non-Local Means denoising cuts random noise while preserving detail, so downstream processing works on clearer document images.

/02Dataset

Trained on real documents

Working with financial institutions and document providers, we built a comprehensive dataset that lifts recognition accuracy.

/03Preprocess

Ready for the model

Reusable scripts automate the preprocessing that shapes each image to the Faster R-CNN model input, saving time and effort.

/04Normalize

Clean, comparable text

Regular expressions strip special characters and accents, standardizing names and ID numbers so extraction and comparison stay reliable.

/ What stood in the way

Four problems, solved without a maintenance window.

Select a challenge to see how it shaped the build.

/01 Varying image quality →
/02 Costly data annotation →
/03 Intricate preprocessing →
/04 Non-standard characters →

Varying image quality

Images from different devices and angles varied in quality and alignment. Denoising and preprocessing brought them to a consistent, usable baseline.

Costly data annotation

Collecting and annotating a KYC dataset is crucial but time-consuming and expensive. We built it in collaboration with financial institutions and document providers.

Intricate preprocessing

Preprocessing data to fit the Faster R-CNN input requirements was intricate. Reusable, automated scripts made image preparation repeatable.

Non-standard characters

Non-standard characters in names and ID numbers made extraction complex. Regular expressions normalized the text for consistent verification.

/ Product screens

The delivered experience.

The interface people actually use.

KYC Verification delivered interface, screen 1/01Focus Areas
KYC Verification delivered interface, screen 2/02Document Upload
KYC Verification delivered interface, screen 3/03Region Detection
KYC Verification delivered interface, screen 4/04Extraction
KYC Verification delivered interface, screen 5/05Verified Result

/ The results

What changed, in numbers.

Clearer Image clarity Non-Local Means denoising reduced noise while preserving detail, improving every later step.
Accurate Region recognition A comprehensive dataset produced a model that accurately recognizes regions of interest on varied KYC documents.
Efficient Automated workflow Reusable scripts and pre-trained region proposal networks streamlined preprocessing and saved time.
Clean Consistent text Regular expressions removed special and non-standard characters, standardizing text and simplifying comparison.

/ Next step

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