| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - object-detection |
| | tags: |
| | - roboflow |
| | - signature |
| | pretty_name: Handwritten Signature |
| | size_categories: |
| | - 1K<n<10K |
| | configs: |
| | - config_name: full |
| | data_files: |
| | - split: train |
| | path: full/train-* |
| | - split: validation |
| | path: full/validation-* |
| | - split: test |
| | path: full/test-* |
| | default: true |
| | dataset_info: |
| | config_name: full |
| | features: |
| | - name: image_id |
| | dtype: int64 |
| | - name: image |
| | dtype: image |
| | - name: width |
| | dtype: int32 |
| | - name: height |
| | dtype: int32 |
| | - name: objects |
| | sequence: |
| | - name: id |
| | dtype: int64 |
| | - name: area |
| | dtype: int64 |
| | - name: bbox |
| | sequence: float32 |
| | length: 4 |
| | - name: category |
| | dtype: |
| | class_label: |
| | names: |
| | '0': signature |
| | splits: |
| | - name: train |
| | num_bytes: 114346924.72 |
| | num_examples: 1980 |
| | - name: validation |
| | num_bytes: 18085018 |
| | num_examples: 420 |
| | - name: test |
| | num_bytes: 18307713 |
| | num_examples: 419 |
| | download_size: 146763157 |
| | dataset_size: 150739655.72 |
| | --- |
| | |
| | # **Dataset: Signature Detection** |
| |
|
| | 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/). |
| |
|
| |  |
| |
|
| | ## **Project Resources Overview** |
| |
|
| | | Resource | Links / Badges | Details | |
| | |---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | **Article** | [](https://huggingface.co/blog/samuellimabraz/signature-detection-model) | A detailed community article covering the full development process of the project | |
| | | **Model Files** | [](https://huggingface.co/tech4humans/yolov8s-signature-detector) | **Available formats:** [](https://pytorch.org/) [](https://onnx.ai/) [](https://developer.nvidia.com/tensorrt) | |
| | | **Dataset – Original** | [](https://universe.roboflow.com/tech-ysdkk/signature-detection-hlx8j) | 2,819 document images annotated with signature coordinates | |
| | | **Dataset – Processed** | [](https://huggingface.co/datasets/tech4humans/signature-detection) | Augmented and pre-processed version (640px) for model training | |
| | | **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) | |
| | | **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 | |
| | | **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) | |
| | | **Live Demo** | [](https://huggingface.co/spaces/tech4humans/signature-detection) | Graphical interface with real-time inference<br> [](https://www.gradio.app/) [](https://plotly.com/python/) | |
| |
|
| | ## Dataset Components |
| |
|
| | 1. **[Tobacco800](https://paperswithcode.com/dataset/tobacco-800):** |
| | - Subset of the Complex Document Image Processing (CDIP) Test Collection. |
| | - Contains scanned images of documents related to the tobacco industry, created by the Illinois Institute of Technology. |
| |
|
| | 2. **[signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up):** |
| | - Part of [Roboflow 100](https://rf100.org/), an Intel initiative. |
| | - Includes 368 annotated images for handwritten signature detection. |
| |
|
| | Both were unified to provide a robust and diverse foundation for object detection tasks. |
| |
|
| | ### **Dataset Details** |
| |
|
| | - **Dataset Split:** |
| | - Training: 1,980 images (70%) |
| | - Validation: 420 images (15%) |
| | - Testing: 419 images (15%) |
| |
|
| | - **Format:** COCO JSON |
| | - **License:** Apache 2.0 |
| |
|
| | ### **Preprocessing and Augmentations** |
| |
|
| | - **Preprocessing:** |
| | - Auto-Orientation: Applied |
| | - Resizing: 640x640 pixels |
| |
|
| | - **Applied Augmentations:** |
| | - 90° Rotation: Clockwise, counterclockwise, and upside down |
| | - Rotation: Between -10° and +10° |
| | - Shearing: ±4° Horizontal, ±3° Vertical |
| | - Brightness: Between -8% and +8% |
| | - Exposure: Between -13% and +13% |
| | - Blur: Up to 1.1 pixels |
| | - Noise: Up to 0.97% of pixels |
| |
|
| | These steps were implemented to enhance the model's robustness and generalization ability. |
| |
|
| | --- |
| |
|
| | ## **Model** |
| |
|
| | 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). |
| |
|
| | --- |
| |
|
| | ## **How to Use with the Datasets Library** |
| |
|
| | This dataset is available on the Hugging Face Hub and can be loaded directly using the `datasets` library. |
| |
|
| | ### **Installing the Library** |
| |
|
| | ```bash |
| | pip install datasets |
| | ``` |
| |
|
| | ### **Loading the Dataset** |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | dataset = load_dataset("samuellimabraz/signature-detection") |
| | |
| | # Visualyze the first sample |
| | print(dataset["train"][0]) |
| | ``` |
| |
|
| | ### **Use Case Example** |
| |
|
| | ```python |
| | import matplotlib.pyplot as plt |
| | import matplotlib.patches as patches |
| | import random |
| | from datasets import load_dataset |
| | |
| | dataset = load_dataset("samuellimabraz/signature-detection") |
| | |
| | # Randomly select a sample from the test set |
| | sample = dataset["test"][random.randint(0, len(dataset["test"]))] |
| | |
| | image = sample["image"] |
| | bboxes = sample["objects"]["bbox"] |
| | |
| | fig, ax = plt.subplots(1, figsize=(8, 8)) |
| | ax.imshow(image) |
| | |
| | for bbox in bboxes: |
| | x, y, width, height = bbox |
| | rect = patches.Rectangle( |
| | (x, y), width, height, linewidth=2, edgecolor="red", facecolor="none" |
| | ) |
| | ax.add_patch(rect) |
| | |
| | plt.axis("off") |
| | plt.show() |
| | ``` |
| |
|
| | --- |
| |
|
| | ## **License** |
| |
|
| | 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. |
| |
|
| | --- |
| |
|
| | ## **Contact and Information** |
| |
|
| | For more information, questions, and contributions, please contact iag@tech4h.com.br. |
| |
|
| | <div align="center"> |
| | <p> |
| | 📧 <b>Email:</b> <a href="mailto:iag@tech4h.com.br">iag@tech4h.com.br</a><br> |
| | 🌐 <b>Website:</b> <a href="https://www.tech4.ai/">www.tech4.ai</a><br> |
| | 💼 <b>LinkedIn:</b> <a href="https://www.linkedin.com/company/tech4humans-hyperautomation/">Tech4Humans</a> |
| | </p> |
| | </div> |
| | |
| | ## **Author** |
| |
|
| | <div align="center"> |
| | <table> |
| | <tr> |
| | <td align="center" width="140"> |
| | <a href="https://huggingface.co/samuellimabraz"> |
| | <img src="https://avatars.githubusercontent.com/u/115582014?s=400&u=c149baf46c51fdee45ad5344cf1b360236d90d09&v=4" width="120" alt="Samuel Lima"/> |
| | <h3>Samuel Lima</h3> |
| | </a> |
| | <p><i>AI Research Engineer</i></p> |
| | <p> |
| | <a href="https://huggingface.co/samuellimabraz"> |
| | <img src="https://img.shields.io/badge/🤗_HuggingFace-samuellimabraz-orange" alt="HuggingFace"/> |
| | </a> |
| | </p> |
| | </td> |
| | <td width="500"> |
| | <h4>Responsibilities in this Project</h4> |
| | <ul> |
| | <li>🔬 Model development and training</li> |
| | <li>📊 Dataset analysis and processing</li> |
| | <li>⚙️ Hyperparameter optimization and performance evaluation</li> |
| | <li>📝 Technical documentation and model card</li> |
| | </ul> |
| | </td> |
| | </tr> |
| | </table> |
| | </div> |
| | |
| | --- |
| |
|
| | <div align="center"> |
| | <p>Developed with 💜 by <a href="https://www.tech4.ai/">Tech4Humans</a></p> |
| | </div> |
