The dataset for extending EMNIST evaluation

The paper describes the dataset for a deeper evaluation of the machine learning models for handwritten character recognition. For that purpose, we build a dataset that, combined with existing NIST Databases, offers possibilities for additional analysis of the models built on these data. The paper summarizes the most popular publicly available machine learning models, trained on the EMNIST-letters dataset. We discuss issues related to the evaluation of state-of-the-art results that have been made by comparing accuracy achieved on the test set built in cross-validation setting. We propose additional evaluation on new, independently constructed data, unaffiliated with the NIST database authors. The dataset and source codes have been made available using Gdansk Tech University repository Most Wiedzy.

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Publication Details

Journal
Scientific Data
Published
2026-01-22
DOI
https://doi.org/10.1038/s41597-025-06291-z
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

The dataset for extending EMNIST evaluation

Kacper Skarżyński, Julian Szymański, Klaudia Ratkowska, Błażej Szutenberg et al.
Scientific Data
Handwritten Text Recognition Techniques
article

The dataset for extending EMNIST evaluation

Kacper Skarżyński, Julian Szymański, Klaudia Ratkowska, Błażej Szutenberg, Szymon Drywa
article en

Abstract

The paper describes the dataset for a deeper evaluation of the machine learning models for handwritten character recognition. For that purpose, we build a dataset that, combined with existing NIST Databases, offers possibilities for additional analysis of the models built on these data. The paper summarizes the most popular publicly available machine learning models, trained on the EMNIST-letters dataset. We discuss issues related to the evaluation of state-of-the-art results that have been made by comparing accuracy achieved on the test set built in cross-validation setting. We propose additional evaluation on new, independently constructed data, unaffiliated with the NIST database authors. The dataset and source codes have been made available using Gdansk Tech University repository Most Wiedzy.

Scientific DataVol. 13(1)
Gdańsk University of Technology (PL), University of Gdańsk (PL)
Politechnika Gdańska
Openalex Percentile: Top 81%
Handwritten Text Recognition Techniques
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