license: mit
task_categories:
- visual-document-retrieval
tags:
- real-data
- lecture-slides
- document-analysis
RealSlide: Benchmark for Lecture Slide Analysis
This repository contains the RealSlide benchmark dataset, a collection of real lecture slides curated to evaluate models for slide element detection and text query-based slide retrieval. The dataset complements the synthetic dataset generated by the SynLecSlideGen pipeline, as presented in the paper AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval. It is designed to test the generalization of models trained on synthetic data to real-world lecture slides.
- Project page: https://synslidegen.github.io/
How to Download:
Using Git via terminal
git lfs install
git clone https://huggingface.co/datasets/nerdyvisky/realslide
Using Python
pip install huggingface_hub
python
from huggingface_hub import snapshot_download
repo_id = "nerdyvisky/realslide" # your full repo path
local_dir = snapshot_download(repo_id=repo_id, repo_type="dataset")
Overview of RealSlide Benchmark
The RealSlide benchmark consists of 1050 real-world lecture slides collected from Creative-Commons licensed graduate lecture slide decks. Full list here Each slide is manually annotated by human-annotators with Slide Object Detection in COCO Format and Text-based slide summary to aid benchmarking VLMs for Slide Image related tasks.
Dataset Components
The dataset includes samples for two main tasks, each with manually verified annotations:
Usage
This dataset can be used for evaluating models trained on synthetic datasets or for fine-tuning models for lecture slide element detection and retrieval. The data is provided with manually verified annotations, making it suitable for benchmarking and performance evaluation.
Citation
If you use this dataset in your research, please cite the corresponding paper:
@article{maniyar2025ai,
title={AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval},
author={Maniyar, Suyash and Trivedi, Vishvesh and Mondal, Ajoy and Mishra, Anand and Jawahar, CV},
journal={arXiv preprint arXiv:2506.23605},
year={2025}
}