dh-unibe/image-text_koenigsfelden-charters-post-1500

Name: dh-unibe/image-text_koenigsfelden-charters-post-1500
Creator: dh-unibe
Published: 2026-04-09 16:20:57
License: 暂无描述

Hugging Face2026-04-09 更新2026-04-05 收录

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https://hf-mirror.com/datasets/dh-unibe/image-text_koenigsfelden-charters-post-1500

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资源简介：

--- dataset_info: config_name: default features: - name: image dtype: image: decode: false - name: xml_content dtype: string - name: filename dtype: string - name: project_name dtype: string splits: - name: train num_examples: 3222 num_bytes: 78902249868 download_size: 78902249868 dataset_size: 78902249868 configs: - config_name: default data_files: - split: train path: data/train/**/*.parquet tags: - image-to-text - htr - trocr - transcription - pagexml license: mit --- # Dataset Card for image-text_koenigsfelden-charters-post-1500 This dataset was created using pagexml-hf converter from Transkribus PageXML data. ## Dataset Summary This dataset contains 3222 samples across 1 split(s). Geographical scope: Switzerland Period: 1291-1550 Languages: Middle High German, Latin Type of document: Documents Provenance: State Archives Aargau ### Projects Included - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_01 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_02 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_03 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_04 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_05 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_06 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_07 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_08 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_09 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_10 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_11 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_12 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_13 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_14 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_15 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_16 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_17 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_18 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_19 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_20 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_21 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_22 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_23 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_24 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_25 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_26 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_27 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_28 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_29 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_30 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_31 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_32 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_33 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_34 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_35 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_36 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_37 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_38 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_39 - FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_40 - 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u-17_0944 - u-17_0945 - u-17_0946 - u-17_0947 - u-17_0948 - u-17_0949 - u-17_0950 - u-17_0951 - u-17_0952 - u-17_0953 - u-17_0954 - u-17_0955 - u-17_0956 - u-17_0957 - u-17_0957a - u-17_0958 - u-17_0959 - u-17_0960 - u-17_0961 - u-17_0962 - u-17_0963_01 - u-17_0963_02 - u-17_0964 - u-17_0965 - u-17_0966 - u-17_0966a - u-17_0967 - u-17_0968 - u-17_0968a - u-17_0968b - u-17_0969 - u-17_0970 - u-17_0970a - u-17_0970b - u-17_0970c - u-17_0970d - u-17_0971 - u-17_0972 - u-17_0973 - u-17_0973a - u-17_0974 - u-17_0975 - u-17_0976 - u-17_0977 - u-17_0978 - u-17_0979 - u-17_0980a - u-17_0980b - u-17_0980c - u-17_0980d - u-17_0981 - u-17_0982 - u-17_0984 - u-17_0985 - u-17_0986 - u-17_0986a - u-17_0987 - u-17_0988 - u-17_0989 - u-17_0990 - u-17_0991 - u-17_0992 - u-17_0993 - u-17_0994 - u-17_0995 - u-17_0996 - u-17_0997 - u-17_0998a - u-17_0998b - u-17_0998c - u-17_0998d - u-17_0998e - u-17_0998f - u-17_0999a - u-17_1000 - u-17_1000a - u-17_1000b - u-17_1001 - u-17_1002 - u-17_1003 - u-17_1004 - u-17_1005 - u-17_1006 - u-17_1007 - u-17_1008_01 - u-17_1008_02 - u-17_1008_03 - u-17_1009 - u-17_1010_01 - u-17_1010_02 - u-17_1011 - u-17_1012 - u-17_1013a - u-17_1013b - u-17_1014 - u-17_1015 - u-17_1016 ## Dataset Structure ### Data Splits - **train**: 3222 samples ### Dataset Size - Approximate total size: 75247.05 MB - Total samples: 3222 ### Features - **image**: `Image(mode=None, decode=False)` - **xml_content**: `Value('string')` - **filename**: `Value('string')` - **project_name**: `Value('string')` ## Data Organization Data is organized as parquet shards by split and project: ``` data/ ├── <split>/ │ └── <project_name>/ │ └── <timestamp>-<shard>.parquet ``` The HuggingFace Hub automatically merges all parquet files when loading the dataset. ## Usage ```python from datasets import load_dataset # Load entire dataset dataset = load_dataset("dh-unibe/image-text_koenigsfelden-charters-post-1500") # Load specific split train_dataset = load_dataset("dh-unibe/image-text_koenigsfelden-charters-post-1500", split="train") ```

提供机构：

dh-unibe

搜集汇总

数据集介绍

构建方式

在历史文献数字化领域，该数据集的构建依托于Transkribus平台生成的PageXML数据，通过pagexml-hf转换器进行系统化处理。这一过程将原始的手写文本图像与其对应的结构化转录内容相结合，确保了数据的一致性与完整性。数据集涵盖了1304个样本，均源自Königsfelden修道院1500年后的特许状项目，这些样本经过精心整理与标注，形成了统一的训练集划分，为后续的学术研究提供了高质量的图像-文本配对资源。

特点

该数据集的核心特征在于其图像与XML内容的紧密关联，每一样本均包含原始文档的高清图像以及对应的PageXML转录文本，这种结构化的数据组织形式便于进行手写文本识别（HTR）与光学字符识别（OCR）任务。数据集中涵盖了多个独立项目，总计超过两百个不同的项目标识，反映了文献来源的多样性与丰富性。此外，数据集以Parquet格式存储，支持高效的数据加载与处理，其约40GB的总规模确保了足够的训练样本量，适用于训练如TrOCR等先进的端到端文本识别模型。

使用方法

利用该数据集进行学术研究时，可通过HuggingFace的datasets库直接加载，使用load_dataset函数并指定数据集名称即可获取完整数据。数据按训练集划分组织，用户可选择加载整个数据集或仅加载训练部分。加载后的数据以标准化的特征字段呈现，包括图像、XML内容、文件名及项目名称，便于直接应用于模型训练或评估。数据集的Parquet分片结构确保了大规模数据的高效读取，支持在分布式计算环境中进行流畅的数据流水线处理。

背景与挑战

背景概述

在数字人文与历史文献学领域，手写文本识别技术对于历史档案的数字化与可检索化具有深远意义。image-text_koenigsfelden-charters-post-1500数据集聚焦于16世纪后瑞士科尼希斯费尔登修道院特许状的手写文档，由伯尔尼大学数字人文中心等机构的研究人员构建，旨在通过图像与文本对齐的数据形式，推动手写文本识别模型的训练与优化。该数据集的核心研究问题在于解决历史手写体因书写风格多样、墨迹褪变及页面破损导致的识别困难，其创建不仅为历史文献的自动化转录提供了关键资源，亦促进了跨学科研究在文化遗产保护方面的应用。

当前挑战

该数据集所针对的手写文本识别领域面临多重挑战：历史手写体因书写者个体差异、墨水扩散及纸张老化而产生字形变异与噪声干扰，使得传统OCR技术难以准确转录；同时，文档中可能存在的缩写符号、古语拼写及版面结构复杂性进一步增加了语义解析的难度。在数据构建过程中，挑战主要源于原始文档的物理状态，如页面污损、折叠痕迹及光照不均导致的图像质量下降，此外，将Transkribus平台中的PageXML标注数据转换为标准化格式时，需确保图像与文本区域的对齐精度，并处理大量异构项目的元数据整合问题。

常用场景

经典使用场景

在数字人文与历史文献学领域，手写文本识别技术正逐步革新档案数字化进程。该数据集作为图像与文本对齐的珍贵资源，其经典使用场景聚焦于训练端到端的手写文本识别模型，特别是针对1500年后科尼希斯费尔登特许状这类历史文档。研究者利用其包含的1304份样本，通过图像与PageXML标注的配对，构建从文档图像到结构化文本的映射，为自动化转录提供高质量的训练基础。

衍生相关工作

围绕该数据集衍生的经典工作主要集中在手写文本识别与文档分析的前沿模型开发。例如，基于Transformer的TrOCR架构常利用此类图像-文本对进行预训练与微调，以提升对历史笔迹的识别鲁棒性。同时，结合PageXML的结构化信息，研究者进一步开发了版面分析与实体抽取的混合模型，推动了从原始图像到语义化知识图谱的完整处理流程创新。

数据集最近研究