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ZENODO
Model . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Model . 2024
License: CC BY
Data sources: Datacite
ZENODO
Model . 2024
License: CC BY
Data sources: Datacite
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HTR Model - Arabic Handwritten Segmentation Model Trained on the Muharaf Corpus

Authors: Bors Uifalean, Simion Toader;

HTR Model - Arabic Handwritten Segmentation Model Trained on the Muharaf Corpus

Abstract

This is a kraken segmentation model trained on 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. Original dataset paper: https://arxiv.org/abs/2406.09630

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average