---
license: other
task_categories:
- image-text-to-text
language:
- zh
size_categories:
- n<1K
---

# PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents

🔗 **Paper:** [https://arxiv.org/abs/2512.23714](https://arxiv.org/abs/2512.23714)

🔗 **Github:** [https://github.com/KevinYuLei/PharmaShip](https://github.com/KevinYuLei/PharmaShip)

### Description

**PharmaShip** is a real-world Chinese dataset of scanned pharmaceutical shipping documents designed to stress-test pre-trained text-layout models under noisy OCR and heterogeneous templates.

It covers three complementary tasks:

* **Sequence Entity Recognition (SER)**
* **Relation Extraction (RE)**
* **Reading Order Prediction (ROP)**

PharmaShip adopts an **entity-centric** evaluation protocol to minimize confounds across architectures and incorporates a directed acyclic reading order graph to capture layout-induced reading strategies.

### Dataset Examples

PharmaShip contains scanned documents with complex tabular layouts, stamps, and handwritten text. We provide fine-grained annotations at the token, entity, and relation levels, as well as reading order supervision.

| Task / View                                                                                            | Visualization                                                                 |
| ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------- |
| **Token-level Annotation** <br>(Visualization of token-level ground truth)                   | ![Token Level Annotation](pics/example/aonuo_char.jpg)                          |
| **Entity-level Annotation** <br>(Semantic entities annotated at the segment level)           | ![Entity Level Annotation](pics/example/aonuo_entity.jpg)                       |
| **Relation Extraction (RE)**<br>(Linkage between entities, e.g., Question-Answer pairs)      | ![Relation Extraction](pics/example/aonuo_link.jpg)                             |
| **Reading Order Prediction (ROP)**<br>(DAG-based reading order based on relation extraction) | ![Reading Order</code></code></code></code>](pics/example/aonuo_segment_ro.jpg) |

### Dataset Statistics

PharmaShip consists of **161** annotated scanned documents with **11,295** segments. The dataset is officially split into 128 samples for training and 33 samples for validation.

Compared to existing datasets like FUNSD, CORD, and SROIE, PharmaShip features a higher density of entities and relations per sample, making it a more challenging benchmark for layout-intensive scenarios.

**Table I:** Statistics of PharmaShip, ROOR, FUNSD, CORD, and SROIE, including words, segments, entities, relation pairs, and the presence/strength of reading-order supervision.

<img src="pics/result/table1.jpg" width="800" alt="Table 1 Statistics">

### Benchmark Results

We benchmarked five representative baselines:  **LiLT** ,  **LayoutLMv3** ,  **GeoLayoutLM** , and their **RORE (Reading-Order-Relation Enhanced)** variants.

The experiments demonstrate that injecting reading-order-oriented regularization consistently improves performance on SER and Entity Linking (EL) tasks.

**Table II:** Performance comparison of different models on SER, EL, and ROP tasks.

<img src="pics/result/table3.jpg" width="600" alt="Table 3 Performance">

*Note: Improvements (↑) denote F1 gains of RORE-enhanced variants. *The RORE enhancement implementation is adapted from *[*ROOR*](https://www.google.com/url?sa=E&source=gmail&q=https://github.com/chongzhangFDU/ROOR "null")*.**

### Access

You can load the dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("YuLeiKevin/PharmaShip")
```

You can also download the full PharmaShip dataset from the link below:

🔗 [**Download PharmaShip Dataset**](https://1drv.ms/u/c/f06402be9f91dc9e/EY6Xzr0c3fVIpb93RidI0NoBfRG3WyrdufkR_7Cv3Y2Hpw)

**Note:** The PharmaShip dataset can only be used for non-commercial research purpose.

### Citation

If you find this dataset helpful for your research, please cite our paper:

```
@misc{xie2025pharmashipentitycentricreadingordersupervisedbenchmark,
      title={PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents}, 
      author={Tingwei Xie and Tianyi Zhou and Yonghong Song},
      year={2025},
      eprint={2512.23714},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.23714}, 
}
```


### Contact

For any questions regarding the dataset or the paper, please contact: [kevinxie@stu.xjtu.edu.cn](mailto:kevinxie@stu.xjtu.edu.cn) or [songyh@xjtu.edu.cn](mailto:songyh@xjtu.edu.cn), *School of Software Engineering, Xi'an Jiaotong University, Xi'an, China*