| --- |
| license: mit |
| language: |
| - ar |
| task_categories: |
| - object-detection |
| - image-to-text |
| tags: |
| - document-understanding |
| - key-information-extraction |
| - receipts |
| - ocr |
| - arabic |
| size_categories: |
| - n<1K |
| --- |
| # AraReceipt |
|
|
| AraReceipt is a manually annotated dataset of **100 Arabic retail receipt images** labeled with **25 key-information classes** plus an `Ignore` category, in [WildReceipt](https://download.openmmlab.com/mmocr/data/wildreceipt.tar) style. It contains **4,609 annotated regions** (46.1 ± 21.6 per image), each with a box, a transcription, and a semantic class. |
|
|
| The dataset was built with [GAIDA](https://github.com/islammesabah/gaida), a human-in-the-loop annotation system that combines OCR-based region extraction, LLM-based semantic pre-annotation, and human validation in Label Studio. Producing AraReceipt required no changes to GAIDA's architecture — only OCR settings for Arabic and mixed-script text, prompt revisions for right-to-left writing, and language-specific field expressions. What's released here is the final result of that process: reviewed, human-corrected annotations only — no model predictions, scores, or annotation-session metadata. |
|
|
| Images are derived from ReceiptSense; AraReceipt is released under the MIT license, matching the terms of the source data. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("IslamMesabah/AraReceipt", split="train") |
| print(ds[0]["id"], len(ds[0]["annotations"])) |
| print(ds[0]["annotations"][0]) |
| ``` |
|
|
| ## Fields |
|
|
| Each row is one receipt image, named `images/0001.jpg` through `images/0100.jpg`. |
|
|
| | Field | Description | |
| | --------------------- | ------------------------------------------------------------ | |
| | `image` | The receipt image. | |
| | `file_name` | Path of the image inside the dataset. | |
| | `id` | Sequential dataset id (1-100), matching the image file name. | |
| | `width`, `height` | Image size in pixels. | |
| | `annotations` | The annotated regions (see below). | |
|
|
| Each entry of `annotations` has: |
|
|
| | Field | Description | |
| | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | `box` | Quadrilateral as`[x1, y1, x2, y2, x3, y3, x4, y4]` in **absolute pixels**, corners ordered top-left, top-right, bottom-right, bottom-left. | |
| | `text` | Transcription of the region. | |
| | `label` | Numeric class id; see`class_list.txt` for the id-to-name mapping (same 26 classes as WildReceipt: 25 key-information classes plus `Ignore`). | |
|
|
| ## Examples |
|
|
| Each region is boxed and labeled with its `label` class name, colored per class. These are the final, human-reviewed annotations. |
|
|
| <p> |
| <img src="examples/example_0001.jpg" alt="AraReceipt example" width="280"> |
| </p> |
|
|
| ## Class distribution |
|
|
| | Class | Regions | Class | Regions | |
| | ------------------- | ------: | ---------------- | ------: | |
| | Others | 1598 | Store_addr_value | 88 | |
| | Prod_price_value | 466 | Date_key | 61 | |
| | Prod_quantity_value | 461 | Tax_value | 45 | |
| | Prod_item_value | 402 | Tel_key | 40 | |
| | Subtotal_value | 225 | Store_addr_key | 39 | |
| | Total_value | 160 | Time_key | 30 | |
| | Total_key | 127 | Tax_key | 21 | |
| | Date_value | 113 | Tips_key | 10 | |
| | Subtotal_key | 110 | Tips_value | 9 | |
| | Store_name_value | 109 | Ignore | 7 | |
| | Prod_price_key | 101 | Store_name_key | 1 | |
| | Time_value | 100 | | | |
| | Prod_item_key | 99 | | | |
| | Prod_quantity_key | 97 | | | |
| | Tel_value | 90 | | | |
|
|
| Slightly over half of the regions contain Arabic script and 43.6% are purely numeric (prices, quantities, totals), reflecting the mixed-script character of Arabic retail receipts. Key-type classes are markedly rarer than their value counterparts, because Arabic receipts often print values without field labels. |
|
|
| ## Annotation process |
|
|
| One native Arabic speaker with professional English proficiency annotated all 100 images using GAIDA: PaddleOCR proposed regions and transcriptions, Llama 4 Maverick proposed a class for each region, and the annotator reviewed, corrected, and approved every region before export. |
|
|
| The end-to-end assisted annotation time averaged 5:51 per image (~10 hours in total). |
|
|
| ## Limitations |
|
|
| The dataset was annotated by a single annotator without an independent second pass, so no inter-annotator agreement is available. Region boundaries originate from OCR detections and were corrected rather than drawn from scratch, which may bias them toward what the detector proposed. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{mesabah2026gaida, |
| title = {GAIDA: Generative AI for Human-in-the-Loop Document Annotation}, |
| author = {Mesabah, Islam and Elhosary, Ali and Sandulu, Priyabanta and |
| Obradovic, Darko and Vollmer, Sebastian J.}, |
| year = {2026} |
| } |
| ``` |
|
|
