|
|
--- |
|
|
license: mit |
|
|
task_categories: |
|
|
- table-question-answering |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- OCR |
|
|
- Tables |
|
|
- IDP |
|
|
size_categories: |
|
|
- n<1K |
|
|
--- |
|
|
|
|
|
This dataset is generated syhthetically to create tables with following characteristics: |
|
|
1. Empty cell percentage in following range [40,70] (Sparse) |
|
|
2. There is clear seperator between rows and columns (Structured). |
|
|
3. 4 <= num rows <= 10, 2 <= num columns <= 6 (Small) |
|
|
|
|
|
### Load the dataset |
|
|
|
|
|
```python |
|
|
import io |
|
|
import pandas as pd |
|
|
from PIL import Image |
|
|
|
|
|
def bytes_to_image(self, image_bytes: bytes): |
|
|
return Image.open(io.BytesIO(image_bytes)) |
|
|
|
|
|
def parse_annotations(self, annotations: str) -> pd.DataFrame: |
|
|
return pd.read_json(StringIO(annotations), orient="records") |
|
|
|
|
|
test_data = load_dataset('nanonets/small_sparse_structured_table', split='test') |
|
|
data_point = test_data[0] |
|
|
image, gt_table = ( |
|
|
bytes_to_image(data_point["images"]), |
|
|
parse_annotations(data_point["annotation"]), |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
|
