  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.
