Omkar2118/datasets huggingface dataset:
READ2016(line-level)
Full name: Line-level Handwritten Text Recognition on READ 2016

This dataset arises from the READ project (Horizon 2020).

The dataset consists of a subset of documents from the Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805 (about 30.000 pages), which will be used in the READ project. This dataset is written in Early Modern German. The number of writers is unknown. Handwriting in this collection is complex enough to challenge the HTR software.

The training dataset is composed of 400 pages; most of the pages consist of a single block with many difficulties for line detection and extraction. The ground-truth in this set is in PAGE format and it is provided annotated at line level in the PAGE files.

The previous dataset is the same that is located at https://zenodo.org/record/218236#.WnLhaCHhBGF

The new file includes the test set corresponding to the HTR competition held at ICFHR 2016

Toselli, A.H., Romero, V., Villegas, M., Vidal, E., & Sánchez, J.A. (2018). HTR Dataset ICFHR 2016 (Version 1.2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297399
LAM(line-level)
Full name: The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition

Handwritten Text Recognition (HTR) is an open
problem at the intersection of Computer Vision and Natural
Language Processing. The main challenges, when dealing with
historical manuscripts, are due to the preservation of the paper
support, the variability of the handwriting – even of the same
author over a wide time-span – and the scarcity of data from
ancient, poorly represented languages. With the aim of fostering
the research on this topic, in this paper we present the Ludovico
Antonio Muratori (LAM) dataset, a large line-level HTR dataset
of Italian ancient manuscripts edited by a single author over
60 years. The dataset comes in two configurations: a basic
splitting and a date-based splitting which takes into account the
age of the author. The first setting is intended to study HTR
on ancient documents in Italian, while the second focuses on
the ability of HTR systems to recognize text written by the
same writer in time periods for which training data are not
available. For both configurations, we analyze quantitative and
qualitative characteristics, also with respect to other line-level
HTR benchmarks, and present the recognition performance of
state-of-the-art HTR architectures. The dataset is available for
download at https://aimagelab.ing.unimore.it/go/lam.
Ultra Fine-Grained Leaves (Cotton, SoyAgeing, SoyGene, SoyGlobal, SoyLocal)

Introduced originally by Xiaohan Yu, Yang Zhao, Yongsheng Gao, Xiaohui Yuan, Shengwu Xiong (2021). Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance. In ICCV 2021.

For visual categorization of leaves at the ultra-fine-grained level, in this case at the cultivar level.

Includes 10 datasets: Cotton, SoyAgeing, SoyGene, SoyGlobal, SoyLocal, SoyAgeingR1, SoyAgeingR3, SoyAgeingR4, SoyAgeingR5, SoyAgeingR6.

Introducing paper: Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance