--- 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} } ```