---
license: cc-by-nc-4.0
language:
- bn
- en
pretty_name: BaFCo
size_categories:
- n<1K
task_categories:
- object-detection
- image-to-text
tags:
- document-understanding
- document-layout-analysis
- key-information-extraction
- forms
- bengali
configs:
- config_name: kie
  data_files:
  - split: all
    path: bafco_kie/all_annotations.json
  - split: en
    path: bafco_kie/en_annotations.json
  - split: bn
    path: bafco_kie/bn_annotations.json
- config_name: dla
  data_files:
  - split: all
    path: bafco_dla/all_annotations.json
---

# BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension (ECCV 2026)

Real Bangladeshi government forms with manual annotations for two tasks: Document Layout
Analysis (DLA) and Key Information Extraction (KIE).

> **This is the dataset and its schema.** The full benchmark pipeline (inference,
> evaluation, and post-processing) lives in the code repository:
> **[github.com/mausulazad/BaFCo](https://github.com/mausulazad/BaFCo)**.
> Paper: [arXiv:2607.05614](https://arxiv.org/abs/2607.05614).

BaFCo curates **200 complex multi-page government forms** across 316 pages and 15 domains.
It provides **16,382 layout entities** and **8,771 inter-field relationships** under a
fine-grained 26-entity schema (with a separate 5-type coarse set) for DLA, and **1,926
key-value pairs** across 156 forms for KIE. The forms come from sectors such as agriculture,
education, banking, and land management.

## Subsets

- **`bafco_dla/`**: Document Layout Analysis. Layout bounding boxes, inter-box relations,
  and form-level labels. Images are partitioned by domain.
- **`bafco_kie/`**: Key Information Extraction. Key-value pairs. Forms are split by language
  (English and Bangla).

Each subset is self-contained: annotations in `all_annotations.json`, images under the
subset folder, and a `README.md` documenting the schema. Image references (`local_images`)
are relative and resolve after download.

## Load

Download the dataset, then read a subset's annotations. Each record is one form; image
paths live in `local_images` as repo-relative strings.

```python
from huggingface_hub import snapshot_download
import os, json

root = snapshot_download("Mausul/bafco", repo_type="dataset")

forms = json.load(open(os.path.join(root, "bafco_dla", "all_annotations.json"), encoding="utf-8"))

# Resolve the first page of the first form to an OS-native file path.
rel = forms[0]["local_images"][0]                      # e.g. "images/<domain>/form_<id>/<page>.jpg"
page_path = os.path.join(root, "bafco_dla", *rel.split("/"))
```

The KIE subset loads the same way from `bafco_kie/all_annotations.json` (or the `en_` /
`bn_` language splits). Each subset's `README.md` documents the full annotation schema.

## Running the benchmark

Inference, evaluation, and post-processing are in the code repository, which reads these
files through a `release_loader.py` adapter. See
[github.com/mausulazad/BaFCo](https://github.com/mausulazad/BaFCo) to reproduce the results.

## License

Code: MIT, Dataset: CC-BY-NC-4.0.