Omkar2118/datasets huggingface dataset:
VideoDB's OCR Benchmark Public Collection
Full name: VideoDB's OCR Benchmark Public Collection

## Dataset Introduction

This dataset leverages VideoDB's Public Collection to offer a diverse range of videos featuring text-containing scenes. It spans multiple categories—ranging from finance and legal documents to software UI elements and handwritten notes—ensuring a broad representation of real-world text appearances. Each video is annotated with frame indexes to facilitate consistent and reproducible OCR benchmarks. Currently, the dataset includes over 25 curated videos, yielding thousands of extracted frames that present a variety of text-related challenges.

### Key Features

1. **Diverse Text Genres**  
    - **Finance/Business:** Includes news tickers and stock market visuals where text scrolls rapidly.  
    - **Legal/Educational:** Features documents with formal language, diagrams, and formatted text.  
    - **Software/Web Development/UI:** Shows on-screen code editors, browser windows, and other UI elements that test OCR's ability to handle varying font sizes and code snippets.  
    - **Handwriting:** Encompasses both cursive and print handwriting on whiteboards or paper, capturing the challenges of style variability and penmanship.  
    - **Miscellaneous/Other:** Covers signage, billboards, and everyday text in the wild.

2. **Rich Annotation**  
    - Each video includes **frame indexes** or **scene timestamps** to ensure consistent, reproducible extraction of text segments.  
    - Ground truth (OCR text) is provided for thousands of extracted frames, facilitating **quantitative performance evaluations** (e.g., CER, WER).

3. **Benchmark-Ready**  
    - The dataset seamlessly integrates with the [ocr-benchmark repository](https://github.com/video-db/ocr-benchmark/) to streamline model evaluation.  
    - Scripts are included for **frame extraction**, **automatic OCR comparison**, and **metric calculation** (CER, WER, accuracy).

### How to Access

- **VideoDB Public Collection ID:** `c-c0a2c223-e377-4625-94bf-910501c2a31c`  
  Simply reference this ID within VideoDB to retrieve and review the videos.
- **Ground Truth Files:** Located in the [`ocr_ground_truths`](https://github.com/video-db/ocr-benchmark/tree/main/ocr_ground_truths) directory. The JSON files map each frame to its corresponding textual annotations.

For detailed instructions on working with VideoDB Public Collections, please refer to the [official documentation](https://docs.videodb.io/public-collections-102).

### Licensing and Usage

- **Usage Restrictions:** The videos are publicly accessible for research and educational use.  
- **Attribution:** Please cite the [Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments](https://arxiv.org/abs/2502.06445) paper or repository if you use this dataset in your work.

Introducing paper: Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments
FSI
Full name: Fluid-Solid interaction

### Data Set Structure

**Fluid Structure Interaction(NS +Elastic wave)**
The `TF_fsi2_results` folder contains simulation data organized by various parameters (`mu`, `x1`, `x2`) where `mu` determines the viscosity and `x1` and `x2` are the parameters of the inlet condition. The dataset includes files for mesh, displacement, velocity, and pressure. 

This dataset structure is detailed below:

```plaintext
TF_fsi2_results/
├── mesh.h5                         # Initial mesh
├── mu=1.0/                         # Simulation results for mu = 1.0
│   ├── x1=-4/                      # Inlet parameter x1 = -4
│   │   ├── x2=-4/                  # Inlet parameter for x2 = -4
│   │   │   └── visualization/      
│   │   │       ├── displacement.h5 # Displacements for mu=1.0, x1=-4, x2=-4
│   │   │       ├── velocity.h5     # Velocity field for mu=1.0, x1=-4, x2=-4
│   │   │       └── pressure.h5     # Pressure field for mu=1.0, x1=-4, x2=-4
│   │   ├── x2=-2/
│   │   │   └── visualization/
│   │   │       ├── displacement.h5
│   │   │       ├── velocity.h5
│   │   │       └── pressure.h5
│   │   └── ...                     # Other x2 values for x1 = -4
│   ├── x1=-2/
│   │   ├── x2=-4/
│   │   │   └── visualization/
│   │   │       ├── displacement.h5
│   │   │       ├── velocity.h5
│   │   │       └── pressure.h5
│   │   └── ...                     # Other x2 values for x1 = -2
│   └── ...                         # Other x1 values for mu = 1.0
├── mu=5.0/                         # Simulation results for mu = 5.0
│   └── ...                         # Similar structure as mu=1.0
└── mu=10.0/                        # Simulation results for mu = 10.0
    └── ...                         # Similar structure as mu=1.0
```
The dataset has `readData.py` and `readMesh.py` for loading the data. Also, the `NsElasticDataset` class in `data_utils/data_loaders.py` loads data automatically for all specified `mu`s and inlet conditions (`x1` and `x2`).

**Fluid Motions with Non-deformable Solid(NS)**
The data is in the folder `TF_cfd2_results`, and the organization is the same as above.