README.md

---
license: cc-by-nc-4.0
language:
- bn
- en
pretty_name: Khondo
size_categories:
- 10K<n<100K
task_categories:
- image-text-to-text
tags:
- document-understanding
- document-packet-splitting
- document-classification
- document-segmentation
- document-recognition
- document-comprehension
- bangla
- cross-lingual
configs:
- config_name: mono_seq
  data_files:
  - split: train
    path: datasets/mono_seq/train.csv
  - split: validation
    path: datasets/mono_seq/validation.csv
  - split: test
    path: datasets/mono_seq/test.csv
- config_name: mono_rand
  data_files:
  - split: train
    path: datasets/mono_rand/train.csv
  - split: validation
    path: datasets/mono_rand/validation.csv
  - split: test
    path: datasets/mono_rand/test.csv
- config_name: poly_seq
  data_files:
  - split: train
    path: datasets/poly_seq/train.csv
  - split: validation
    path: datasets/poly_seq/validation.csv
  - split: test
    path: datasets/poly_seq/test.csv
- config_name: poly_rand
  data_files:
  - split: train
    path: datasets/poly_rand/train.csv
  - split: validation
    path: datasets/poly_rand/validation.csv
  - split: test
    path: datasets/poly_rand/test.csv
- config_name: poly_int
  data_files:
  - split: train
    path: datasets/poly_int/train.csv
  - split: validation
    path: datasets/poly_int/validation.csv
  - split: test
    path: datasets/poly_int/test.csv
---

# Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms

Real Bangla and English government forms assembled into **document packets** for the
packet-splitting task: given a packet of concatenated form pages, recover which pages
belong to each source document and restore each document's original page order.

> **This is the dataset and its schema.** The full benchmark pipeline (inference,
> evaluation, and analysis) lives in the code repository:
> **[github.com/mausulazad/Khondo](https://github.com/mausulazad/Khondo)**.
> Paper: [arXiv:2607.21780](http://arxiv.org/abs/2607.21780).

Khondo provides **1,950 packets**, **390 per variant** across **5 concatenation
schemes**, built from **423 real government forms** across **14 administrative
domains**. It is bilingual (Bangla and English) and vision-native: models read the page
images directly, without OCR. Each variant is split train/validation/test at the form
level, with no form shared across splits.

## Variants

Five packet-construction strategies of increasing difficulty. Each packet concatenates
pages from one or more source forms; shuffled variants permute the page order.

| Variant | Composition | Page order |
|---|---|---|
| `mono_seq` | one document type per packet | sequential |
| `mono_rand` | one document type per packet | shuffled |
| `poly_seq` | multiple document types | sequential |
| `poly_rand` | multiple document types | shuffled |
| `poly_int` | multiple document types | round-robin interleaved |

## Structure

```
datasets/
  <variant>/ground_truth_json/<train|validation|test>/*.json    # one packet per file (authoritative ground truth)
  <variant>/<train|validation|test>.csv                         # row-per-page flattening (dataset viewer)
  xling/<bn|en>/<variant>/ground_truth_json/test/*.json         # cross-lingual strata (test only, JSON only)
images/
  <domain>/<form>/pNN.jpg                                       # form page images
```

The five `config`s above expose the per-variant CSVs to the dataset viewer and
`load_dataset`. The packet ground truth (`ground_truth_json/`) and the cross-lingual
strata (`xling/`) are JSON files, read directly rather than through `load_dataset`.

## Data formats

### Ground-truth JSON

One file per packet, named by `doc_id`. This is the authoritative ground truth for
evaluation.

| Field | Description |
|---|---|
| `doc_id` | UUID of the packet |
| `variant` | one of the five strategies above |
| `total_pages` | number of pages in the packet |
| `num_subdocuments` | number of source forms combined into the packet |
| `subdocuments[]` | the constituent forms, each with `doc_type_id` (domain), `local_doc_id`, `group_id`, `page_ordinals` (positions within the packet), and `pages[]` |
| `subdocuments[].pages[]` | per page: `page` (index in the packet), `original_doc_name`, `image_path`, `local_doc_id_page_ordinal` (order within the source document) |

`image_path` is relative to the download root, so it resolves directly after
`snapshot_download`.

### CSV

A row-per-page flattening of the packets, with columns `doc_type`, `parent_doc_name`
(the packet UUID), `local_doc_id`, `page`, `image_path`, and `group_id`. Page order for
shuffled variants comes from the ground-truth JSON.

## Cross-lingual strata

`datasets/xling/` provides size-matched monolingual test packets, Bangla-only (`bn`) and
English-only (`en`), built with the same construction strategies, 100 packets per
language per variant. They hold packet structure fixed while varying language, for
measuring how packet-splitting performance shifts across languages.

## Load

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

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

gt_dir = os.path.join(root, "datasets", "mono_seq", "ground_truth_json", "test")
packet = json.load(open(glob.glob(os.path.join(gt_dir, "*.json"))[0], encoding="utf-8"))

# image_path is repo-relative and resolves under `root`.
rel = packet["subdocuments"][0]["pages"][0]["image_path"]   # e.g. "images/agriculture/004/p01.jpg"
page_path = os.path.join(root, *rel.split("/"))
```

The per-variant CSVs load the same way through `load_dataset("Mausul/khondo", "mono_seq")`.

## Reproducing the benchmark

Inference, evaluation, and analysis live in the code repository. Download the dataset
into the repository root so `datasets/` and `images/` sit beside the code, or point
`KHONDO_DATA_ROOT` at the download location. Model predictions ship with the code, so the
published numbers reproduce without re-running inference. See
[github.com/mausulazad/Khondo](https://github.com/mausulazad/Khondo).

## Source and attribution

Khondo adapts the packet-splitting methodology of **DocSplit** (Islam et al.,
arXiv:2602.15958; [`amazon/doc_split`](https://huggingface.co/datasets/amazon/doc_split),
CC-BY-NC-4.0) for Bangla and English forms, and adds the cross-lingual strata.

## License

Code: MIT. Dataset: CC-BY-NC-4.0, following DocSplit and RVL-CDIP-N-MP.

## Citation

```bibtex
@misc{azad2026khondomultimodalbenchmarkdocument,
  title={Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms}, 
  author={Abu Tyeb Azad and Fahim Ahmed and Ishita Sur Apan and Ezharuddin Jubaer and Sumaiya Karim Katha and Armun Alam and Amin Ahsan Ali and Aman Chadha and Md Mofijul Islam and AKM Mahbubur Rahman},
  year={2026},
  eprint={2607.21780},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2607.21780}, 
}
```