Deep Learning for Historical Document Analysis and Recognition—A Survey

Lombardi, Francesco; Marinai, Simone

doi:10.3390/jimaging6100110

Cited by 55 publications

(20 citation statements)

References 104 publications

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“…While the use of machine learning for image enhancement is well established [178], its application to document image enhancement, in particular historical, is still rare [72,74,115,130,131]. Notwithstanding results comparable to the state of the art, the challenges it faces are considerable.…”

Section: Computer Science Researchmentioning

confidence: 99%

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

Atanasiu

Marthot-Santaniello

2021

IJDAR

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This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.

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Section: Computer Science Researchmentioning

confidence: 99%

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

Atanasiu

Marthot-Santaniello

2021

IJDAR

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“…This task was split into document analysis and document understanding, related to the layout structure and logical structure of a document, respectively. Lombardi and Marinai [94] surveyed papers that use deep learning methods for historical document image analysis by showing the connections between input and output for all methods and according to task. This paper further presented some historical datasets.…”

Section: Related Workmentioning

confidence: 99%

“…In the last decade, deep neural networks have been the state-of-the-art in challenging domains [94].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

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This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 56 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or semantic analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

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“…This growing interest has led to the publication of numerous and diverse approaches for solving these tasks. While non Deep Learning based methods are still proposed [5], there is a growing trend of using the power of Deep Learning based methods, as in many other computer science fields [24]. Even with the growing amount of annotated data available in this field, there is most of the time not enough data to properly train Deep Learning models with the goal of generalization.…”

Section: Related Workmentioning

confidence: 99%

Self-supervised deep metric learning for ancient papyrus fragments retrieval

Pirrone

Beurton-Aimar

Journet

2021

IJDAR

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This work focuses on document fragments association using Deep Metric Learning methods. More precisely, we are interested in ancient papyri fragments that need to be reconstructed prior to their analysis by papyrologists. This is a challenging task to automatize using machine learning algorithms because labeled data is rare, often incomplete, imbalanced and of inconsistent conservation states. However, there is a real need for such software in the papyrology community as the process of reconstructing the papyri by hand is extremely time consuming and tedious. In this paper, we explore ways in which papyrologists can obtain useful matching suggestion on new data using Deep Convolutional Siamese-Networks. We emphasize on low-to-no human intervention for annotating images. We show that the from-scratch self-supervised approach we propose is more effective than using knowledge transfer from a large dataset, the former achieving a top-1 accuracy score of 0.73 on a retrieval task involving 800 fragments.The research leading to this results has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under grant agreement No 758907 and is part of the GESHAEM project, hosted by the Ausonius Institute. The source code (upon request) and data used in this article is available at https://morphoboid.labri.fr/ self-supervised-papyrus.html

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Deep Learning for Historical Document Analysis and Recognition—A Survey

Cited by 55 publications

References 104 publications

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

A Survey of Historical Document Image Datasets

Self-supervised deep metric learning for ancient papyrus fragments retrieval

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