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--- |
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license: apache-2.0 |
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task_categories: |
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- object-detection |
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tags: |
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- roboflow |
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- signature |
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pretty_name: Handwritten Signature |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: full |
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data_files: |
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- split: train |
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path: full/train-* |
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- split: validation |
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path: full/validation-* |
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- split: test |
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path: full/test-* |
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default: true |
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dataset_info: |
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config_name: full |
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features: |
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- name: image_id |
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dtype: int64 |
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- name: image |
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dtype: image |
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- name: width |
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dtype: int32 |
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- name: height |
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dtype: int32 |
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- name: objects |
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sequence: |
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- name: id |
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dtype: int64 |
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- name: area |
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dtype: int64 |
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- name: bbox |
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sequence: float32 |
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length: 4 |
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- name: category |
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dtype: |
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class_label: |
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names: |
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'0': signature |
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splits: |
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- name: train |
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num_bytes: 114346924.72 |
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num_examples: 1980 |
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- name: validation |
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num_bytes: 18085018 |
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num_examples: 420 |
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- name: test |
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num_bytes: 18307713 |
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num_examples: 419 |
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download_size: 146763157 |
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dataset_size: 150739655.72 |
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--- |
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# **Dataset: Signature Detection** |
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This dataset was developed to train models for handwritten signature detection in various types of documents. It combines data from two public datasets ([Tobacco800](https://paperswithcode.com/dataset/tobacco-800) and [signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up)) with processing and unification performed in [Roboflow](https://roboflow.com/). |
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## **Project Resources Overview** |
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| Resource | Links / Badges | Details | |
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|---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| **Article** | [](https://huggingface.co/blog/samuellimabraz/signature-detection-model) | A detailed community article covering the full development process of the project | |
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| **Model Files** | [](https://huggingface.co/tech4humans/yolov8s-signature-detector) | **Available formats:** [](https://pytorch.org/) [](https://onnx.ai/) [](https://developer.nvidia.com/tensorrt) | |
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| **Dataset – Original** | [](https://universe.roboflow.com/tech-ysdkk/signature-detection-hlx8j) | 2,819 document images annotated with signature coordinates | |
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| **Dataset – Processed** | [](https://huggingface.co/datasets/tech4humans/signature-detection) | Augmented and pre-processed version (640px) for model training | |
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| **Notebooks – Model Experiments** | [](https://colab.research.google.com/drive/1wSySw_zwyuv6XSaGmkngI4dwbj-hR4ix) [](https://api.wandb.ai/links/samuel-lima-tech4humans/30cmrkp8) | Complete training and evaluation pipeline with selection among different architectures (yolo, detr, rt-detr, conditional-detr, yolos) | |
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| **Notebooks – HP Tuning** | [](https://colab.research.google.com/drive/1wSySw_zwyuv6XSaGmkngI4dwbj-hR4ix) [](https://api.wandb.ai/links/samuel-lima-tech4humans/31a6zhb1) | Optuna trials for optimizing the precision/recall balance | |
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| **Inference Server** | [](https://github.com/tech4ai/t4ai-signature-detect-server) | Complete deployment and inference pipeline with Triton Inference Server<br> [](https://docs.openvino.ai/2025/index.html) [](https://www.docker.com/) [](https://developer.nvidia.com/triton-inference-server) | |
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| **Live Demo** | [](https://huggingface.co/spaces/tech4humans/signature-detection) | Graphical interface with real-time inference<br> [](https://www.gradio.app/) [](https://plotly.com/python/) | |
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## Dataset Components |
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1. **[Tobacco800](https://paperswithcode.com/dataset/tobacco-800):** |
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- Subset of the Complex Document Image Processing (CDIP) Test Collection. |
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- Contains scanned images of documents related to the tobacco industry, created by the Illinois Institute of Technology. |
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2. **[signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up):** |
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- Part of [Roboflow 100](https://rf100.org/), an Intel initiative. |
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- Includes 368 annotated images for handwritten signature detection. |
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Both were unified to provide a robust and diverse foundation for object detection tasks. |
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### **Dataset Details** |
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- **Dataset Split:** |
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- Training: 1,980 images (70%) |
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- Validation: 420 images (15%) |
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- Testing: 419 images (15%) |
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- **Format:** COCO JSON |
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- **License:** Apache 2.0 |
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### **Preprocessing and Augmentations** |
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- **Preprocessing:** |
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- Auto-Orientation: Applied |
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- Resizing: 640x640 pixels |
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- **Applied Augmentations:** |
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- 90° Rotation: Clockwise, counterclockwise, and upside down |
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- Rotation: Between -10° and +10° |
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- Shearing: ±4° Horizontal, ±3° Vertical |
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- Brightness: Between -8% and +8% |
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- Exposure: Between -13% and +13% |
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- Blur: Up to 1.1 pixels |
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- Noise: Up to 0.97% of pixels |
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These steps were implemented to enhance the model's robustness and generalization ability. |
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--- |
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## **Model** |
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This dataset was used to train the [yolov8s-signature-detector](https://huggingface.co/tech4humans/yolov8s-signature-detector) model for handwritten signature detection. For full technical details including performance metrics and architecture specifications, see the [Model Card](https://huggingface.co/tech4humans/yolov8s-signature-detector). |
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--- |
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## **How to Use with the Datasets Library** |
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This dataset is available on the Hugging Face Hub and can be loaded directly using the `datasets` library. |
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### **Installing the Library** |
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```bash |
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pip install datasets |
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``` |
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### **Loading the Dataset** |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("samuellimabraz/signature-detection") |
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# Visualyze the first sample |
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print(dataset["train"][0]) |
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``` |
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### **Use Case Example** |
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```python |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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import random |
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from datasets import load_dataset |
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dataset = load_dataset("samuellimabraz/signature-detection") |
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# Randomly select a sample from the test set |
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sample = dataset["test"][random.randint(0, len(dataset["test"]))] |
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image = sample["image"] |
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bboxes = sample["objects"]["bbox"] |
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fig, ax = plt.subplots(1, figsize=(8, 8)) |
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ax.imshow(image) |
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for bbox in bboxes: |
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x, y, width, height = bbox |
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rect = patches.Rectangle( |
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(x, y), width, height, linewidth=2, edgecolor="red", facecolor="none" |
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) |
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ax.add_patch(rect) |
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plt.axis("off") |
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plt.show() |
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``` |
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--- |
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## **License** |
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The dataset is distributed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license. You are free to use, modify, and distribute the dataset as long as you comply with the license terms. |
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--- |
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## **Contact and Information** |
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For more information, questions, and contributions, please contact [email protected]. |
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<div align="center"> |
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<p> |
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📧 <b>Email:</b> <a href="mailto:[email protected]">[email protected]</a><br> |
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🌐 <b>Website:</b> <a href="https://www.tech4.ai/">www.tech4.ai</a><br> |
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💼 <b>LinkedIn:</b> <a href="https://www.linkedin.com/company/tech4humans-hyperautomation/">Tech4Humans</a> |
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</p> |
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</div> |
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## **Author** |
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<div align="center"> |
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<table> |
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<tr> |
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<td align="center" width="140"> |
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<a href="https://huggingface.co/samuellimabraz"> |
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<img src="https://avatars.githubusercontent.com/u/115582014?s=400&u=c149baf46c51fdee45ad5344cf1b360236d90d09&v=4" width="120" alt="Samuel Lima"/> |
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<h3>Samuel Lima</h3> |
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</a> |
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<p><i>AI Research Engineer</i></p> |
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<p> |
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<a href="https://huggingface.co/samuellimabraz"> |
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<img src="https://img.shields.io/badge/🤗_HuggingFace-samuellimabraz-orange" alt="HuggingFace"/> |
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</a> |
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</p> |
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</td> |
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<td width="500"> |
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<h4>Responsibilities in this Project</h4> |
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<ul> |
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<li>🔬 Model development and training</li> |
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<li>📊 Dataset analysis and processing</li> |
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<li>⚙️ Hyperparameter optimization and performance evaluation</li> |
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<li>📝 Technical documentation and model card</li> |
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</ul> |
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</td> |
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</tr> |
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</table> |
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</div> |
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--- |
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<div align="center"> |
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<p>Developed with 💜 by <a href="https://www.tech4.ai/">Tech4Humans</a></p> |
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</div> |
