AmjadKha/HandSignatureDetection

✍️ Handwritten Signature Classifier

AI-powered handwritten signature recognition for document processing

An image-classification model designed to distinguish handwritten signatures from non-signature content in scanned documents and document images.

Built on the MobileNetV3-Small architecture, this model is intended for efficient inference and integration into automated document-processing workflows.

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🚀 Overview

The model classifies an input image into one of two categories:

Class	Description
SIGNATURE	Handwritten signature or signature-like handwriting
NOT_SIGNATURE	Non-signature document content, such as printed text, dates, numbers, and other visual elements

Its primary purpose is to help document-processing systems identify genuine signature regions and reduce false-positive detections.

🧠 Model Architecture

• Architecture: MobileNetV3-Small
• Base Model: qualcomm/MobileNet-v3-Small
• Task: Binary image classification
• Framework: Transformers
• Input: RGB image
• Output: Class probabilities and predicted label
• Classes: SIGNATURE / NOT_SIGNATURE

MobileNetV3-Small provides a lightweight architecture suitable for resource-constrained environments and desktop inference.

📚 Training Dataset

Dataset: AmjadKha/HandWrittenSigntureDataset

The model uses a handwritten signature dataset for training and evaluation.

Supported document languages:

• English
• Arabic

📊 Evaluation Metrics

The model is evaluated using the following metrics:

Metric	Purpose
Accuracy	Measures overall classification correctness
Precision	Measures how many predicted signatures are actually signatures
Recall	Measures how many actual signatures are detected
F1-score	Balances precision and recall

Actual evaluation scores should be added after testing on a separate held-out test dataset.

⚙️ Installation

pip install transformers torch pillow

💻 Usage

from transformers import pipeline

classifier = pipeline(
    "image-classification",
    model="AmjadKha/YOUR-MODEL-REPOSITORY"
)

results = classifier("signature.png")

for result in results:
    print(result["label"], result["score"])

Replace YOUR-MODEL-REPOSITORY with the actual Hugging Face model repository name.

🏦 Intended Applications

• Banking document processing
• Signature verification workflows
• Account-opening documentation
• Customer onboarding documents
• Automated document classification
• PDF signature-region filtering
• Intelligent document processing (IDP)

⚠️ Limitations

• Classification does not establish the legal authenticity of a signature.
• Handwritten names and initials may resemble signatures.
• Performance depends on image quality, resolution, and training-data diversity.
• The model should be evaluated on representative documents before production deployment.
• It should be combined with signature detection and verification mechanisms for high-assurance workflows.

📄 License

Apache License 2.0.

👨‍💻 Author

Amjad Khaliliah

Hugging Face: @AmjadKha

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Lightweight computer vision for intelligent document processing.