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...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Cascianelli, Silvia, Pippi, Vittorio, Maarand, Martin, Cornia, Marcella, Baraldi, Lorenzo, Kermorvant, Christopher, Cucchiara, Rita
Format: Article
Language:English
Subjects:
Benchmarks
Computer vision
Configurations
Datasets
Handwriting recognition
Natural language processing
Qualitative analysis
Splitting
Online Access:Get full text
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Summary: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 \url{https://aimagelab.ing.unimore.it/go/lam}.
ISSN:2331-8422