The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition

• URL: http://arxiv.org/abs/2208.07682v1
• Date: Tue, 16 Aug 2022 11:44:16 GMT
• Title: The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition
• Authors: Silvia Cascianelli, Vittorio Pippi, Martin Maarand, Marcella Cornia, Lorenzo Baraldi, Christopher Kermorvant, Rita Cucchiara
• Abstract summary: Handwritten Text Recognition (HTR) is an open problem at the intersection of Computer Vision and Natural Language Processing. We present the Ludovico Antonio Muratori dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years.
• Score: 40.20527158935902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

Related papers

• Handwritten Text Recognition: A Survey [9.121437356699358]
  Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early-based approaches to modern state-of-the-art neural models.
  arXiv  Detail & Related papers  (2025-02-12T13:59:37Z)
• PLATTER: A Page-Level Handwritten Text Recognition System for Indic Scripts [20.394597266150534]
  We present an end-to-end framework for Page-Level hAndwriTTen TExt Recognition (PLATTER) Secondly, we demonstrate the usage of PLATTER to measure the performance of our language-agnostic HTD model. Finally, we release a Corpus of Handwritten Indic Scripts (CHIPS), a meticulously curated, page-level Indic handwritten OCR dataset.
  arXiv  Detail & Related papers  (2025-02-10T05:50:26Z)
• Detecting Document-level Paraphrased Machine Generated Content: Mimicking Human Writing Style and Involving Discourse Features [57.34477506004105]
  Machine-generated content poses challenges such as academic plagiarism and the spread of misinformation. We introduce novel methodologies and datasets to overcome these challenges. We propose MhBART, an encoder-decoder model designed to emulate human writing style. We also propose DTransformer, a model that integrates discourse analysis through PDTB preprocessing to encode structural features.
  arXiv  Detail & Related papers  (2024-12-17T08:47:41Z)
• Boosting Punctuation Restoration with Data Generation and Reinforcement Learning [70.26450819702728]
  Punctuation restoration is an important task in automatic speech recognition (ASR) The discrepancy between written punctuated texts and ASR texts limits the usability of written texts in training punctuation restoration systems for ASR texts. This paper proposes a reinforcement learning method to exploit in-topic written texts and recent advances in large pre-trained generative language models to bridge this gap.
  arXiv  Detail & Related papers  (2023-07-24T17:22:04Z)
• CiteBench: A benchmark for Scientific Citation Text Generation [69.37571393032026]
  CiteBench is a benchmark for citation text generation. We make the code for CiteBench publicly available at https://github.com/UKPLab/citebench.
  arXiv  Detail & Related papers  (2022-12-19T16:10:56Z)
• Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions [52.250269529057014]
  Handwritten Text Recognition (HTR) in free-volution pages is a challenging image understanding task. We propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text.
  arXiv  Detail & Related papers  (2022-08-17T06:55:54Z)
• StackMix and Blot Augmentations for Handwritten Text Recognition [0.0]
  The paper describes the architecture of the neural net-work and two ways of increasing the volume of train-ing data. StackMix can also be applied to the standalone task of gen-erating handwritten text based on printed text.
  arXiv  Detail & Related papers  (2021-08-26T09:28:22Z)
• One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition [10.473427493876422]
  Low resource Handwritten Text Recognition is a hard problem due to the scarce annotated data and the very limited linguistic information. In this paper we address this problem through a data generation technique based on Bayesian Program Learning. Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet.
  arXiv  Detail & Related papers  (2021-05-11T18:53:01Z)
• Handwriting Classification for the Analysis of Art-Historical Documents [6.918282834668529]
  We focus on the analysis of handwriting in scanned documents from the art-historic archive of the WPI. We propose a handwriting classification model that labels extracted text fragments based on their visual structure.
  arXiv  Detail & Related papers  (2020-11-04T13:06:46Z)
• Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
  We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
  arXiv  Detail & Related papers  (2020-02-24T12:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.