mlsanityData to 7 Oct 2026

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

arXiv 2208.07682 ↗First posted 16 Aug 2022cs.CV cs.DLcited by 16

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Abstract (arXiv)

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 urlhttps://aimagelab.ing.unimore.it/go/lam.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 27 of its 65 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

27 of 65 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  7. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  8. 2017Deformable Convolutional Networkscited by 1,481
  9. 2015Spatial Transformer Networkscited by 1,423
  10. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 519
  11. 2021TrOCR: Transformer-based Optical Character Recognition with Pre-trained Modelscited by 278
  12. 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 137
15 more
  1. 2020Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognitioncited by 71
  2. 2019Evaluating Sequence-to-Sequence Models for Handwritten Text Recognitioncited by 58
  3. 2020OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfoldcited by 53
  4. 2016Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognitioncited by 46
  5. 2016Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attentioncited by 45
  6. 2021Rethinking Text Line Recognition Modelscited by 45
  7. 2013Dropout improves Recurrent Neural Networks for Handwriting Recognitioncited by 39
  8. 2018Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networkscited by 31
  9. 2018An Efficient End-to-End Neural Model for Handwritten Text Recognitioncited by 25
  10. 2020Recurrence-free unconstrained handwritten text recognition using gated fully convolutional networkcited by 23
  11. 2020Boosting offline handwritten text recognition in historical documents with few labeled linescited by 19
  12. 2021Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixescited by 18
  13. 2019KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learningcited by 13
  14. 2018Are 2D-LSTM really dead for offline text recognition?cited by 7
  15. 2019No Padding Please: Efficient Neural Handwriting Recognitioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 3; 2026: 8.

16 papers.

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  1. 2024HTR-VT: Handwritten Text Recognition with Vision Transformercited by 24
  2. 2023HWD: A Novel Evaluation Score for Styled Handwritten Text Generationcited by 19
  3. 2023How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuningcited by 13
  4. 2025Benchmarking Large Language Models for Handwritten Text Recognitioncited by 11
  5. 2023Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyricited by 2
  6. 2025Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?cited by 2
  7. 2025HTR-ConvText: Leveraging Convolution and Textual Information for Handwritten Text Recognitioncited by 1
  8. 2026METATR: A Multilingual, Evolving Benchmark for Automatic Text Recognitioncited by 1
  9. 2026A Text Recognition Dataset from Sahidic Coptic Ancient Manuscriptscited by 1
  10. 2023CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognitioncited by 0
  11. 2026FP-THD: Full page transcription of historical documentscited by 0
  12. 2026PoM: A Linear-Time Replacement for Attention with the Polynomial Mixercited by 0
4 more
  1. 2026Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogiescited by 0
  2. 2026UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscriptscited by 0
  3. 2026Handwritten Text Recognition Lives in the High-Pixel Variance Subspacecited by 0
  4. 2026Exploring In-Context Learning for Handwritten Text Recognitioncited by 0