--- license: apache-2.0 task_categories: - object-detection language: - pt tags: - roboflow - signature pretty_name: Handwritten Signature size_categories: - 1K Dataset on HF 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/). ![](./assets/roboflow_ds.png) ## 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 as the foundation for training the model [tech4humans/yolov8s-signature-detector](https://huggingface.co/tech4humans/yolov8s-signature-detector), designed to identify handwritten signatures in documents with high precision. For more details about the model, including metrics, architecture, and usage instructions, visit the [Model Card](https://huggingface.co/tech4humans/signature-detection). [![Model on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-lg.svg)](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.

📧 Email: iag@tech4h.com.br
🌐 Website: www.tech4.ai
💼 LinkedIn: Tech4Humans

## **Author**
Samuel Lima

Samuel Lima

AI Research Engineer

HuggingFace

Responsibilities in this Project

  • 🔬 Model development and training
  • 📊 Dataset analysis and processing
  • ⚙️ Hyperparameter optimization and performance evaluation
  • 📝 Technical documentation and model card
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Developed with ❤️ by Tech4Humans