PPT-seg Instance Segmentation Dataset and Pre-Trained Model by Aditya

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Explore Dataset

Here are a few use cases for this project:

Automated PowerPoint Design Refinement: Designers or agencies could use PPT-seg to automatically analyze PowerPoint designs and suggest improvements based on identified elements. The model could notice repetitive elements, analyze the effective use of images, titles, and shapes and provide corrective feedback.
Accessibility Compliance: Institutions or organizations could use PPT-seg to ensure PowerPoint presentations comply with accessibility guidelines. The model could identify instances where necessary elements like alt-text for images or tables are missing and suggest corrections.
PowerPoint Search Engine: PPT-seg could be the foundation of a search engine for PowerPoint presentations. By identifying the elements within a presentation, users could perform more granular searches, such as looking for presentations with a particular type of chart or table.
Presentation Content Analyzer: PPT-seg could be used for analyzing the content of presentations in an educational or corporate context. It could help institutions identify presentation trends, such as common topics or preferred design elements, aiding in the development of better learning or presentation guidelines.
Automated Copyright Compliance: PPT-seg could aid organizations in staying compliant with copyright rules by identifying copyrighted images or charts within presentations. By comparing elements against a database of copyrighted assets, it could bring potential infringements to the user's attention.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            ppt-seg-kdxas_dataset,
                            title = { PPT-seg Dataset },
                            type = { Open Source Dataset },
                            author = { Aditya },
                            howpublished = { \url{ https://universe.roboflow.com/aditya-jrqy6/ppt-seg-kdxas } },
                            url = { https://universe.roboflow.com/aditya-jrqy6/ppt-seg-kdxas },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-06-11 },
                            }