We present the Manuscripts of Handwritten Arabic~(Muharaf) dataset, which is
a machine learning dataset consisting of more than 1,600 historic handwritten
page images transcribed by experts in archival Arabic. Each document image is
accompanied by spatial polygonal coordinates of its text lines as well as basic
page elements. This dataset was compiled to advance the state of the art in
handwritten text recognition (HTR), not only for Arabic manuscripts but also
for cursive text in general. The Muharaf dataset includes diverse handwriting
styles and a wide range of document types, including personal letters, diaries,
notes, poems, church records, and legal correspondences. In this paper, we
describe the data acquisition pipeline, notable dataset features, and
statistics. We also provide a preliminary baseline result achieved by training
convolutional neural networks using this data.

我们提供了一个由历史手写页面图像组成的机器学习数据集，每个文档图像配有文字行的空间多边形坐标以及基本页面元素，可用于提升手写文本识别的技术水平。该数据集包含不同的手写风格和各种文档类型，并介绍了数据获取过程、数据集特征和统计信息，同时提供了使用此数据进行训练的卷积神经网络的初步基线结果。

Muharaf：手写阿拉伯文连续文本识别的手稿数据集

Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text  Recognition

Segmentation of Arabic manuscripts into lines of text and words is an
important step to make recognition systems more efficient and accurate. The
problem of segmentation into text lines is solved since there are carefully
annotated dataset dedicated to this task. However, To the best of our
knowledge, there are no dataset annotating the word position of Arabic texts.
In this paper, we present a new dataset specifically designed for historical
Arabic script in which we annotate position in word level.

将阿拉伯手稿分割成文本行和单词是使识别系统更高效准确的重要步骤。我们提出了一个新的数据集，专门设计用于历史阿拉伯脚本，并在单词层面进行位置标注。