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--- |
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license: other |
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task_categories: |
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- image-text-to-text |
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language: |
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- zh |
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size_categories: |
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- n<1K |
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--- |
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# PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents |
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🔗 **Paper:** [https://arxiv.org/abs/2512.23714](https://arxiv.org/abs/2512.23714) |
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🔗 **Github:** [https://github.com/KevinYuLei/PharmaShip](https://github.com/KevinYuLei/PharmaShip) |
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### Description |
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**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. |
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It covers three complementary tasks: |
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* **Sequence Entity Recognition (SER)** |
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* **Relation Extraction (RE)** |
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* **Reading Order Prediction (ROP)** |
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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. |
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### Dataset Examples |
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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. |
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| Task / View | Visualization | |
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| ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------- | |
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| **Token-level Annotation** <br>(Visualization of token-level ground truth) |  | |
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| **Entity-level Annotation** <br>(Semantic entities annotated at the segment level) |  | |
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| **Relation Extraction (RE)**<br>(Linkage between entities, e.g., Question-Answer pairs) |  | |
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| **Reading Order Prediction (ROP)**<br>(DAG-based reading order based on relation extraction) |  | |
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### Dataset Statistics |
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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. |
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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. |
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**Table I:** Statistics of PharmaShip, ROOR, FUNSD, CORD, and SROIE, including words, segments, entities, relation pairs, and the presence/strength of reading-order supervision. |
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<img src="pics/result/table1.jpg" width="800" alt="Table 1 Statistics"> |
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### Benchmark Results |
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We benchmarked five representative baselines: **LiLT** , **LayoutLMv3** , **GeoLayoutLM** , and their **RORE (Reading-Order-Relation Enhanced)** variants. |
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The experiments demonstrate that injecting reading-order-oriented regularization consistently improves performance on SER and Entity Linking (EL) tasks. |
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**Table II:** Performance comparison of different models on SER, EL, and ROP tasks. |
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<img src="pics/result/table3.jpg" width="600" alt="Table 3 Performance"> |
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*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")*.** |
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### Access |
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You can load the dataset directly using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("YuLeiKevin/PharmaShip") |
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``` |
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You can also download the full PharmaShip dataset from the link below: |
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🔗 [**Download PharmaShip Dataset**](https://1drv.ms/u/c/f06402be9f91dc9e/EY6Xzr0c3fVIpb93RidI0NoBfRG3WyrdufkR_7Cv3Y2Hpw) |
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**Note:** The PharmaShip dataset can only be used for non-commercial research purpose. |
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### Citation |
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If you find this dataset helpful for your research, please cite our paper: |
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``` |
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@misc{xie2025pharmashipentitycentricreadingordersupervisedbenchmark, |
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title={PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents}, |
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author={Tingwei Xie and Tianyi Zhou and Yonghong Song}, |
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year={2025}, |
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eprint={2512.23714}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2512.23714}, |
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} |
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``` |
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### Contact |
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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* |
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