CIawevy/TextPecker-1.5M

--- license: apache-2.0 task_categories: - image-to-text dataset_info: features: - name: id dtype: string - name: images list: image - name: conversations list: - name: content dtype: string - name: role dtype: string - name: data_source dtype: string - name: class dtype: string - name: ori_bbox list: string splits: - name: test num_bytes: 986226411 num_examples: 1061 - name: train num_bytes: 984872941236 num_examples: 1482028 download_size: 985226675892 dataset_size: 985859167647 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- # TextPecker-1.5M: A Dataset for Training and evaluating TextPecker This repository contains the **TextPecker-1.5M** dataset, a new benchmark proposed in the paper "[TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering](https://arxiv.org/abs/2602.20903)". ## Code and Project Page The official implementation and project details for the TextPecker and TextPecker-1.5M dataset can be found on the GitHub repository: [https://github.com/CIawevy/TextPecker](https://github.com/CIawevy/TextPecker) ## Sample Usage You can easily load the TextPecker-1.5M dataset using the Hugging Face `datasets` library. The dataset is provided in two configurations: `train` and `test` ```python from datasets import load_dataset # Load the full TextPecker-1.5M dataset (includes train and test splits) dataset = load_dataset("CIawevy/TextPecker-1.5M", "default") train_data = dataset["train"] test_data = dataset["test"] # Load specific split directly (more efficient for practical usage) train_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="train") test_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="test") ``` For detailed instructions on installation, model download, evaluation, and running demos, please refer to the [GitHub repository](https://github.com/CIawevy/TextPecker). ## Citation If you find this dataset useful for your research, please cite the accompanying paper: ```bibtex @article{zhu2026TextPecker, title = {TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering}, author = {Zhu, Hanshen and Liu, Yuliang and Wu, Xuecheng and Wang, An-Lan and Feng, Hao and Yang, Dingkang and Feng, Chao and Huang, Can and Tang, Jingqun and Bai, Xiang}, journal = {arXiv preprint arXiv:2602.20903}, year = {2026} } ```许可证：apache-2.0 任务类别： - 图像到文本（image-to-text） 数据集信息： 特征： - 名称：id 数据类型：字符串 - 名称：images 数据类型：图像列表 - 名称：conversations 数据类型：列表，元素为： - 名称：content 数据类型：字符串 - 名称：role 数据类型：字符串 - 名称：data_source 数据类型：字符串 - 名称：class 数据类型：字符串 - 名称：ori_bbox（原始边界框） 数据类型：字符串列表 数据拆分： - 名称：test 字节数：986226411 样本数量：1061 - 名称：train 字节数：984872941236 样本数量：1482028 下载大小：985226675892 数据集总大小：985859167647 配置项： - 配置名称：default 数据文件： - 拆分：test 路径：data/test-* - 拆分：train 路径：data/train-* # TextPecker-1.5M：用于训练与评估TextPecker的数据集 本仓库包含**TextPecker-1.5M**数据集，即论文《TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering》（arXiv预印本链接：https://arxiv.org/abs/2602.20903）中提出的全新基准数据集。 ## 代码与项目页面 TextPecker与TextPecker-1.5M数据集的官方实现及项目详情可访问以下GitHub仓库获取： [https://github.com/CIawevy/TextPecker](https://github.com/CIawevy/TextPecker) ## 示例用法 您可通过Hugging Face的`datasets`库便捷加载TextPecker-1.5M数据集。该数据集提供两种配置：`train`与`test`。 python from datasets import load_dataset # 加载完整的TextPecker-1.5M数据集（包含训练集与测试集拆分） dataset = load_dataset("CIawevy/TextPecker-1.5M", "default") train_data = dataset["train"] test_data = dataset["test"] # 直接加载指定拆分（实际使用时更高效） train_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="train") test_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="test") 如需了解安装、模型下载、评估及运行演示的详细说明，请参阅[GitHub仓库](https://github.com/CIawevy/TextPecker)。 ## 引用 若您的研究工作中用到了本数据集，请引用相关论文： bibtex @article{zhu2026TextPecker, title = {TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering}, author = {Zhu, Hanshen and Liu, Yuliang and Wu, Xuecheng and Wang, An-Lan and Feng, Hao and Yang, Dingkang and Feng, Chao and Huang, Can and Tang, Jingqun and Bai, Xiang}, journal = {arXiv preprint arXiv:2602.20903}, year = {2026} }