README.md

---
license: other
license_name: idrbt-dataset-license
license_link: https://www.idrbt.ac.in
language:
  - en
tags:
  - object-detection
  - document-understanding
  - cheque-processing
  - bounding-box
  - ocr
  - banking
  - indian-banking
  - annotations-only
task_categories:
  - object-detection
pretty_name: IDRBT Cheque Field Annotations
size_categories:
  - n<1K
annotations_creators:
  - expert-generated
source_datasets:
  - original
dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: filename
      dtype: string
    - name: image_width
      dtype: int32
    - name: image_height
      dtype: int32
    - name: date
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
    - name: amount
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
    - name: ifsc
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
    - name: acno
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
    - name: sign
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
    - name: name
      dtype:
        struct:
          - name: xmin
            dtype: int32
          - name: ymin
            dtype: int32
          - name: xmax
            dtype: int32
          - name: ymax
            dtype: int32
  splits:
    - name: train
      num_bytes: 16384
      num_examples: 90
    - name: validation
      num_bytes: 2048
      num_examples: 11
    - name: test
      num_bytes: 2048
      num_examples: 11
  download_size: 20480
  dataset_size: 112
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: validation
        path: data/validation-*.parquet
      - split: test
        path: data/test-*.parquet
---

# IDRBT Cheque Field Annotations

Bounding-box annotations for six standard fields in Indian bank cheques,
derived from the publicly released
[IDRBT Cheque Image Dataset](https://www.idrbt.ac.in).

> **Images are not included.** This dataset contains annotations only.
> The original TIFF images must be obtained directly from IDRBT under
> their terms of use. Filenames in this dataset correspond 1-to-1 with
> the images in the IDRBT release.

---

## Dataset Summary

| Property | Value |
|---|---|
| Total annotated cheques | 112 |
| Splits | Train 90 / Validation 11 / Test 11 |
| Fields per cheque | 6 (fixed) |
| Annotation format | Bounding box [xmin, ymin, xmax, ymax] (absolute pixels) |
| Original image format | TIFF, RGB, ~2365 × 1087 px |
| Original annotation format | Pascal VOC XML |

---

## Fields

Each cheque is annotated with exactly one bounding box per field:

| Field | Key | Description |
|-------|-----|-------------|
| Date | `date` | Cheque date (typically top-right) |
| Amount (figures) | `amount` | Numeric amount (right column) |
| IFSC / branch code | `ifsc` | Bank branch identifier (mid-left) |
| Account number | `acno` | Full account number (centre) |
| Signature | `sign` | Handwritten signature region (bottom-right) |
| Payee name | `name` | "Pay to" name (full-width band) |

---

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `image_id` | `string` | Cheque identifier (filename without extension) |
| `filename` | `string` | Original TIFF filename (e.g. `Cheque 083654.tif`) |
| `image_width` | `int32` | Original image width in pixels |
| `image_height` | `int32` | Original image height in pixels |
| `date` | `struct` | `{xmin, ymin, xmax, ymax}` |
| `amount` | `struct` | `{xmin, ymin, xmax, ymax}` |
| `ifsc` | `struct` | `{xmin, ymin, xmax, ymax}` |
| `acno` | `struct` | `{xmin, ymin, xmax, ymax}` |
| `sign` | `struct` | `{xmin, ymin, xmax, ymax}` |
| `name` | `struct` | `{xmin, ymin, xmax, ymax}` |

All coordinates are in **absolute pixels** relative to the original image.

### Data Splits

| Split | Examples |
|-------|---------|
| Train | 90 |
| Validation | 11 |
| Test | 11 |
| **Total** | **112** |

Splits are reproducible (random seed 42).

---

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("jaganadhg/cheque-field-annotations")
print(dataset)
# DatasetDict({
#     train:      Dataset({features: [...], num_rows: 90}),
#     validation: Dataset({features: [...], num_rows: 11}),
#     test:       Dataset({features: [...], num_rows: 11})
# })

sample = dataset["train"][0]
print(sample["filename"])          # 'Cheque 083654.tif'
print(sample["image_width"])       # 2372
print(sample["date"])              # {'xmin': 1762, 'ymin': 65, 'xmax': 2329, 'ymax': 186}
```

### Convert to COCO format

```python
from datasets import load_dataset

FIELD_NAMES = ["date", "amount", "ifsc", "acno", "sign", "name"]
LABEL2ID    = {f: i + 1 for i, f in enumerate(FIELD_NAMES)}  # 1-indexed

dataset = load_dataset("jaganadhg/cheque-field-annotations")

def to_coco_row(example):
    """Convert one dataset row to a COCO-style annotation list."""
    annotations = []
    for field in FIELD_NAMES:
        bb = example[field]
        w  = bb["xmax"] - bb["xmin"]
        h  = bb["ymax"] - bb["ymin"]
        annotations.append({
            "category_id": LABEL2ID[field],
            "category":    field,
            "bbox":        [bb["xmin"], bb["ymin"], w, h],   # COCO: [x, y, w, h]
            "area":        w * h,
            "iscrowd":     0,
        })
    example["annotations"] = annotations
    return example

coco_dataset = dataset.map(to_coco_row)
```

### Normalise coordinates for model training

```python
def normalise(example):
    """Normalise boxes to [0,1] relative to image dimensions."""
    W, H = example["image_width"], example["image_height"]
    for field in ["date", "amount", "ifsc", "acno", "sign", "name"]:
        bb = example[field]
        example[f"{field}_norm"] = {
            "xmin": bb["xmin"] / W,
            "ymin": bb["ymin"] / H,
            "xmax": bb["xmax"] / W,
            "ymax": bb["ymax"] / H,
        }
    return example

normalised = dataset.map(normalise)
```

### Use with IDRBT images (after downloading)

```python
import os
from datasets import load_dataset
from PIL import Image

IMAGE_DIR = "/path/to/IDRBT_Cheque_Image_Dataset/300"
dataset   = load_dataset("jaganadhg/cheque-field-annotations")

def add_image(example):
    img_path = os.path.join(IMAGE_DIR, example["filename"])
    example["image"] = Image.open(img_path).convert("RGB")
    return example

dataset_with_images = dataset.map(add_image)
```

---

## Field Layout

The spatial distribution of fields across cheques (normalised coordinates,
mean ± std over all 112 cheques):

| Field | Centre-x | Centre-y | Width | Height |
|-------|----------|----------|-------|--------|
| date | 0.85 ± 0.01 | 0.13 ± 0.01 | 0.26 ± 0.02 | 0.14 ± 0.01 |
| amount | 0.85 ± 0.01 | 0.42 ± 0.01 | 0.27 ± 0.01 | 0.14 ± 0.01 |
| ifsc | 0.22 ± 0.01 | 0.16 ± 0.01 | 0.13 ± 0.01 | 0.05 ± 0.01 |
| acno | 0.21 ± 0.05 | 0.53 ± 0.02 | 0.32 ± 0.05 | 0.11 ± 0.02 |
| sign | 0.90 ± 0.02 | 0.72 ± 0.04 | 0.14 ± 0.02 | 0.22 ± 0.04 |
| name | 0.51 ± 0.01 | 0.25 ± 0.01 | 0.96 ± 0.01 | 0.11 ± 0.01 |

Cheques follow a consistent layout: `name` is a full-width band near the top,
`date` and `amount` are stacked on the right, `ifsc` and `acno` span the
centre-left, and `sign` sits in the bottom-right.

---

## Benchmark Results

A ResNet-50 regression model trained on the same annotations achieves the
following on a held-out test set of 10 images. (The model was trained from
an earlier HDF5 consolidation of these annotations in which 7 rows were
corrupted and excluded — 105 usable images, 85/10/10 split — so its splits
differ slightly from the 90/11/11 splits of this release.)

| Field | IoU | Acc@0.5 |
|-------|-----|---------|
| date | 0.528 | 50% |
| amount | 0.572 | 80% |
| ifsc | 0.506 | 60% |
| acno | 0.579 | 90% |
| sign | 0.437 | 30% |
| name | 0.658 | 90% |
| **Mean** | **0.547** | **67%** |

These are indicative single-run numbers, not a strong benchmark. A no-learning
baseline that predicts each field's mean training box already reaches **0.691
mIoU / 80% accuracy** on this task, so cheque layout is highly regular and
absolute IoU should be read with that prior in mind. See the accompanying
paper for the full controlled analysis (including a negative result on
synthetic-data augmentation).

Pre-trained model: [`jaganadhg/cheque-field-regressor`](https://huggingface.co/jaganadhg/cheque-field-regressor)

---

## Source Data

Original dataset published by the **Institute for Development and Research
in Banking Technology (IDRBT)**, Hyderabad, India.

- **Original release**: IDRBT Cheque Image Dataset
- **Original URL**: https://www.idrbt.ac.in
- **Original format**: TIFF images + Pascal VOC XML annotations

Annotations were converted from Pascal VOC XML to Parquet format for this
release. No image data is included.

---

## License

The annotations in this dataset are derived from the IDRBT Cheque Image
Dataset. Please refer to [IDRBT's terms of use](https://www.idrbt.ac.in)
before using this dataset for commercial purposes.

---

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{idrbt-cheque-annotations-2026,
  title        = {IDRBT Cheque Field Annotations},
  author       = {Gopinadhan, Jaganadh},
  year         = {2026},
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/datasets/jaganadhg/cheque-field-annotations},
  note         = {Annotations derived from the IDRBT Cheque Image Dataset}
}
```

For the original IDRBT dataset, please also cite:

```bibtex
@misc{idrbt-cheque-dataset,
  title        = {IDRBT Cheque Image Dataset},
  author       = {{Institute for Development and Research in Banking Technology}},
  howpublished = {\url{https://www.idrbt.ac.in}},
  year         = {2020}
}
```