| --- |
| license: mit |
| task_categories: |
| - object-detection |
| tags: |
| - image |
| - computer-vision |
| - document-analysis |
| - financial-documents |
| - table-detection |
| - yolo |
| - synthetic-data |
| pretty_name: Synthetic Bank Statement Table Detection Dataset |
| size_categories: |
| - n<10k |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| - name: category |
| list: int64 |
| splits: |
| - name: train |
| num_bytes: 1855469904.4 |
| num_examples: 8475 |
| download_size: 1813512839 |
| dataset_size: 1855469904.4 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # Synthetic Bank Statement Table Detection Dataset |
|
|
| A synthetically generated collection of bank statement images with pixel-perfect, automatically generated bounding box annotations for **table detection**. |
|
|
| > 🔑 **In one sentence:** fake bank statements + auto-generated YOLO labels for table localization, designed for training document layout and table-detection models. |
|
|
| --- |
|
|
| ## At a Glance |
|
|
| | | | |
| |---|---| |
| | **Task** | Object Detection → Table Detection | |
| | **Format** | PNG images + YOLO-format `.txt` labels | |
| | **Classes** | 1 (`0` = Table) | |
| | **Total images** | 8475 | |
| | **Splits** | None yet — single unsplit set | |
| | **Source** | 100% synthetic, generated with Python + ReportLab | |
| | **Real data?** | ❌ No real bank statements, customers, or financial records | |
| | **License** | MIT | |
|
|
| --- |
|
|
| ## Why This Dataset Exists |
|
|
| Table detection is the first stage of many document AI pipelines. Before OCR, table structure recognition, or information extraction can begin, the document's table must first be accurately localized. |
|
|
| This dataset models **clean, digitally-generated bank statement PDFs** — the kind produced directly by a bank's own statement-generation system and downloaded as a native PDF (not a scanned paper document). Instead of manually drawing bounding boxes after generation, the table coordinates are captured directly from the ReportLab rendering engine as each document is created. |
|
|
| Because every annotation is produced during rendering, the resulting labels are pixel-perfect and free from manual annotation errors. The dataset is intended for pretraining, benchmarking, or augmenting table detection models before fine-tuning on real financial documents. |
|
|
| --- |
|
|
| ## What's Inside |
|
|
| Each sample consists of: |
|
|
| - A synthetic bank statement image |
| - One YOLO annotation file |
| - A bounding box surrounding the complete transaction table |
|
|
| ### Layout diversity |
|
|
| The generator produces a wide variety of layouts, including: |
|
|
| - Portrait and landscape orientations |
| - Multiple banking templates |
| - Different table widths and positions |
| - Bordered, borderless, and zebra-striped tables |
| - Variable row counts |
| - Multi-page statements |
| - Randomized customer information and transaction histories |
|
|
| --- |
|
|
| ## Folder Structure |
|
|
| ```text |
| table_detection/ |
| ├── images/ |
| │ ├── anon_stmt_001_p1.png |
| │ ├── anon_stmt_001_p2.png |
| │ ├── anon_stmt_002_p1.png |
| │ └── ... |
| └── labels/ |
| ├── anon_stmt_001_p1.txt |
| ├── anon_stmt_001_p2.txt |
| ├── anon_stmt_002_p1.txt |
| └── ... |
| ``` |
|
|
| Each image has a matching YOLO label file with the same base filename. |
|
|
| **Filename convention:** `anon_stmt_<statement_id>_p<page_number>` |
|
|
| - `anon_` — synthetic/anonymized document |
| - `stmt_<statement_id>` — unique statement identifier |
| - `p<page_number>` — page number |
|
|
| Multi-page statements are stored as separate image/label pairs. |
|
|
| --- |
|
|
| ## Label Format |
|
|
| Standard YOLO object detection format: |
|
|
| ```text |
| class_id center_x center_y width height |
| ``` |
|
|
| - `class_id`: `0` (Table) |
| - Coordinates are normalized to the image width and height. |
|
|
| | Class ID | Label | Description | |
| |:--------:|-------|-------------| |
| | `0` | Table | Bounding box surrounding the complete transaction table | |
|
|
| --- |
|
|
| ## Data Example |
|
|
| The examples below show raw statement images and the contents of their matching YOLO label files. |
|
|
| <table> |
| <tr> |
| <th>Image</th> |
| <th>Image</th> |
| </tr> |
| <tr> |
| <td> |
|
|
|  |
|
|
| </td> |
| <td> |
|
|
|  |
|
|
| </td> |
| </tr> |
| <tr> |
| <th>Label</th> |
| <th>Label</th> |
| </tr> |
| <tr> |
| <td> |
|
|
| ```text |
| 0 0.503 0.548 0.856 0.701 |
| ``` |
|
|
| </td> |
| <td> |
|
|
| ```text |
| 0 0.498 0.541 0.842 0.688 |
| ``` |
|
|
| </td> |
| </tr> |
| </table> |
|
|
| Each image typically contains one bounding box representing the entire transaction table. |
|
|
| --- |
|
|
| <details> |
| <summary><strong>Load with the Hugging Face Hub</strong></summary> |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| local_dir = snapshot_download( |
| repo_id="Panhapich/Panhapich/bank-statement-detection", |
| repo_type="dataset" |
| ) |
| |
| print(local_dir) |
| ``` |
|
|
| </details> |
|
|
| <details> |
| <summary><strong>Train with Ultralytics YOLO</strong></summary> |
|
|
| ```yaml |
| path: ./table_detection |
| |
| train: images |
| val: images |
| |
| nc: 1 |
| |
| names: |
| - table |
| ``` |
|
|
| ```python |
| from ultralytics import YOLO |
| |
| model = YOLO("yolov8n.pt") |
| |
| model.train( |
| data="data.yaml", |
| epochs=50, |
| imgsz=1024 |
| ) |
| ``` |
|
|
| </details> |
|
|
| --- |
|
|
| ## Annotation Methodology |
|
|
| Every annotation is generated automatically during document creation: |
|
|
| 1. A synthetic bank statement is procedurally generated. |
| 2. ReportLab renders the transaction table. |
| 3. The exact table coordinates are captured during rendering. |
| 4. The coordinates are converted into normalized YOLO format and written to the matching label file. |
|
|
| Because annotations originate directly from the rendering engine, they provide pixel-perfect ground truth with zero manual labeling. |
|
|
| --- |
|
|
| ## Compatible Models |
|
|
| - YOLOv5 |
| - YOLOv8 |
| - YOLOv9 |
| - YOLOv10 |
| - RT-DETR |
| - DETR |
| - Faster R-CNN |
| - Table Transformer (table localization stage) |
| - Other object detection architectures |
|
|
| --- |
|
|
| ## Intended Applications |
|
|
| - Table Detection |
| - Document Layout Analysis |
| - Intelligent Document Processing (IDP) |
| - OCR preprocessing |
| - Financial document understanding |
| - Table extraction pipelines |
| - Document AI benchmarking |
|
|
| --- |
|
|
| ## Limitations & Intended Use |
|
|
| - Models clean, digitally-generated bank statement PDFs only. |
| - Does not simulate scanned or photographed paper documents. |
| - Synthetic templates cannot fully represent the diversity of real bank statement layouts. |
| - Contains no real customer information or financial records. |
| - Single unsplit dataset. |
|
|
| **Best used for:** pretraining, benchmarking, or data augmentation before fine-tuning on real bank statement documents. |
|
|
| --- |
|
|
| ## Dataset Origin |
|
|
| This dataset is entirely synthetic. Every document was procedurally generated using Python and ReportLab. No real bank statements, customer information, financial records, proprietary templates, or institution-specific branding are included. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{synthetic_bank_statement_table_detection, |
| title = {Synthetic Bank Statement Table Detection Dataset}, |
| author = {Uk, Panhapich}, |
| year = {2026}, |
| note = {Synthetically generated for table detection research} |
| } |
| ``` |
|
|
| ## License |
|
|
| Released under the **MIT License**. |
