Document to Signature Verification Pipeline - Banking Case Study

Signature verification in banking is not only a model classification problem. Before a signature can be compared, it must be reliably extracted from heterogeneous financial documents such as PDFs, TIFFs, scans, and image-based forms. The first challenge was data preparation. Source documents came in different formats, resolutions, scan qualities, aspect ratios, compression levels, and visual noise conditions. A technically important part of the work was converting each document into a high-quality image representation that downstream computer vision models could process without damaging the original signature geometry. This required careful preprocessing decisions: preserving aspect ratio, avoiding lossy transformations, converting documents into controlled black-and-white or grayscale representations where appropriate, cropping regions without degrading stroke quality, and keeping the signature structure visually stable for the final verification model. The second challenge was the visual complexity of banking documents. Signatures were not always clean or isolated. They could appear near handwriting, stamps, printed text, form lines, background artifacts, scan noise, or other visual clutter. A direct signature similarity model would be fragile if the extracted signature region contained too much background contamination. The third challenge was limited and imbalanced signature data. Genuine and forged signature samples are naturally difficult to collect at scale, and the variation between genuine signatures from the same person can be high. This made data augmentation and robustness testing an important part of the workflow. The pipeline therefore had to solve several connected problems: - Convert PDF/TIFF and scanned documents into high-quality image representations. - Preserve aspect ratio, signature geometry, stroke structure, and visual fidelity during preprocessing. - Detect the signature region accurately on complex financial documents. - Crop the detected region using model coordinates without meaningful image quality loss. - Clean the extracted signature image from handwriting, stamps, lines, and background artifacts. - Explore GAN- and diffusion-based synthetic data strategies to improve robustness under limited and imbalanced data conditions. - Feed the cleaned signature image into CNN/Siamese-based models for similarity analysis and forgery detection. - Evaluate the complete workflow through threshold behavior, false-positive / false-negative trade-offs, and model benchmarking. This made the work an end-to-end document understanding and computer vision engineering problem rather than a single-model experiment. Details are generalized due to confidentiality. No internal banking data, document samples, business rules, model weights, thresholds, or infrastructure details are disclosed.

The solution was designed as a multi-stage computer vision pipeline for extracting, cleaning, and verifying signatures from banking documents. The first stage focused on document-to-image conversion and preprocessing. PDF/TIFF and document-based inputs were converted into high-quality image representations with careful control over resolution, aspect ratio, color mode, and compression behavior. This stage was technically critical because even small distortions during conversion, resizing, binarization, or cropping could damage signature strokes and reduce the reliability of the final verification model. After preprocessing, a fine-tuned YOLO-based object detection model was used to locate signature regions on the document. The detector achieved above 99% performance in internal validation for signature region detection. Custom cropping logic then used YOLO coordinate outputs to extract the signature area while preserving image quality and avoiding unnecessary resizing or compression artifacts. The cropped signature region was then passed into a fine-tuned U-Net segmentation model. This stage was designed to clean the extracted signature by suppressing visual noise such as handwriting, stamps, printed text, document lines, scanning artifacts, and background contamination. The segmentation step improved the quality of the signature image before it reached the final similarity model. To make the multi-model pipeline easier to run in a shared inference environment, trained models were converted to ONNX format where appropriate. This helped reduce framework and dependency friction between different model components and made it easier to standardize inference execution across detection, segmentation, and verification stages. The ONNX conversion step was especially useful because the pipeline combined multiple model families and required consistent runtime behavior rather than isolated notebook-style execution. Data preparation and robustness were also treated as first-class parts of the pipeline. Because signature datasets can be limited, imbalanced, and visually inconsistent, synthetic data strategies were explored using GAN- and diffusion-based approaches. These methods were used to support augmentation experiments, improve model robustness, and benchmark how different model families behaved under constrained data conditions. The final verification stage used CNN/Siamese-based models for signature similarity analysis and forgery detection. These models were designed and evaluated after the upstream extraction and cleanup stages, rather than directly on raw document crops. This made the final comparison more reliable because the input signature images were more controlled, cleaner, and less affected by document noise. The complete workflow combined document preprocessing, object detection, coordinate-based cropping, segmentation-based cleanup, synthetic data augmentation, model benchmarking, and CNN/Siamese similarity modeling into a single end-to-end ML engineering pipeline.

The work produced an end-to-end document-to-signature verification pipeline for a risk-sensitive banking use case. Key outcomes included: - Built a complete preprocessing workflow for converting PDF/TIFF and scanned banking documents into high-quality image representations suitable for computer vision models. - Solved document preparation issues related to resolution, aspect ratio, black-and-white / grayscale conversion, scan noise, compression artifacts, and signature geometry preservation. - Fine-tuned a YOLO-based model for signature region detection, achieving above 99% performance in internal validation. - Developed custom coordinate-based cropping logic using YOLO outputs to extract signature regions while avoiding meaningful image quality loss. - Fine-tuned a U-Net segmentation model to clean extracted signature images by removing handwriting, stamps, form lines, document artifacts, and other background contamination. - Used GAN- and diffusion-based synthetic data strategies during data preparation and augmentation experiments to improve robustness under limited and imbalanced signature data conditions. - Designed and evaluated CNN/Siamese-based models for signature similarity verification and forgery detection. - Benchmarked multiple model families across detection, segmentation, augmentation, and verification stages. - Improved the reliability of the final signature verification process by treating upstream document extraction, image quality preservation, and signature cleanup as critical parts of the ML system. - Supported risk-sensitive evaluation through threshold analysis, false-positive / false-negative reasoning, model comparison, and practical decision-boundary analysis. - Converted trained model components to ONNX format where appropriate to improve inference interoperability, reduce dependency conflicts, and support a more consistent multi-model runtime environment. The main impact was transforming signature verification from a standalone similarity model into a controlled, multi-stage computer vision pipeline where document quality, detection accuracy, segmentation cleanup, synthetic augmentation, and verification modeling were engineered together.