The paper introduces a software tool developed for refining the ground truth data of the DIVA-HisDB dataset, which is used for training machine learning models on historical document analysis. The software, implemented in Python, offers various tools for manipulating and representing images and ground truths, facilitating the addition of new layout classes, resizing, cropping, and more, thereby providing a flexible and extendable framework for researchers. It aims to improve the efficiency and reusability of ground truth development for historical documents, emphasizing a user-friendly architecture that allows for custom algorithm implementation for document analysis. The approach addresses challenges in document image analysis (DIA) by offering a solution that mitigates the need for extensive manual ground truth annotation, leveraging vector and pixel-based ground truth representations, and introducing functionalities for image processing and manipulation.
- Layout Classes Management: Easily manage and define layout classes to categorize different elements within your documents, such as text and decorations. 🏷️
- Image Tools: Get your images ready with simple yet effective cropping, resizing, and layering tools, ensuring your ground truth data is just right. 🖼️
- Plug-and-Play Architecture: Enjoy a modular design that’s easy to get along with. Need to tweak something or add a new feature? No sweat! 🛠️
- Design Patterns at Work: We use common design patterns like Builder and Visitor to keep things organized and adaptable without complicating your life. 🧩
- Support for Various Formats: Work with different data formats effortlessly, thanks to comprehensive input/output support that plays nice with your existing workflow. 🔄
- User-Friendly: Ground Truth Refiner is built for users of all backgrounds. Whether you're deep into research or just getting started with document analysis, you'll find it approachable and easy to use. 🤗