Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition

TL;DR

The Muharaf dataset offers a comprehensive collection of over 1,600 historic handwritten Arabic pages with detailed annotations, aiming to improve handwritten text recognition for Arabic and cursive scripts.

Contribution

This paper introduces the Muharaf dataset, a diverse and annotated collection of handwritten Arabic manuscripts designed to advance HTR research.

Findings

Baseline CNN results on the dataset demonstrate its utility.

The dataset covers various handwriting styles and document types.

Data acquisition pipeline ensures high-quality annotations.

Abstract

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.