A One-Shot Learning Approach to Document Layout Segmentation of Ancient Arabic Manuscripts

De Nardin, Axel; Zottin, Silvia; Piciarelli, Claudio; Colombi, Emanuela; Foresti, Gian Luca

doi:10.1109/wacv57701.2024.00794

Cited by 2 publications

(2 citation statements)

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“…This integration fully exploits the limited dataset available while still achieving competitive performance compared to other supervised methods, as highlighted in their in-depth and analytical work [9]. A further more recent approach is the one presented in [11], where a one-shot learning approach is introduced for the layout segmentation of ancient Arabic documents. In this paper, the authors introduce an efficient framework that, despite being trained on only one labeled page per manuscript, the framework achieves state-of-theart performance compared to other approaches tested on a challenging dataset of ancient Arabic manuscripts.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years, this problem has been tackled by various authors [9][10][11], who developed a set of few-shot-learning-oriented frameworks specifically aiming at leveraging the small amount of data available to generate more and more accurate predictions for the task at hand, producing results that are on par or even surpass previously available state-of-the-art approaches that relied on much more data. In the present paper, we tackle the problem from another point of view by exploring different transfer learning approaches as a way to make good use of alternative data sources to pre-train our models.…”

Section: Introductionmentioning

confidence: 99%

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In-domain versus Out-of-domain transfer learning for document layout analysis

De Nardin,

Zottin,

Piciarelli

et al. 2024

Preprint

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Data availability is a big concern in the field of document analysis, especially when working on tasks that require a high degree of precision when it comes to the definition of the ground truths on which to train deep learning models. A notable example is represented by the task of Document Layout Analysis in handwritten documents, which requires pixel-precise segmentation maps to highlight the different layout components of each document page. These segmentation maps are typically very time-consuming and require a high degree of domain knowledge to be defined, as they are intrinsically characterized by the content of the text. For this reason in the present work, we explore the effects of different initialization strategies for deep learning models employed for this type of task by relying on both in-domain and cross-domain datasets for their pre-training. To test the employed models we use 2 publicly available datasets with heterogeneous characteristics both regarding their structure as well as the languages of the contained documents. We show how a combination of cross-domain and in-domain transfer learning approaches leads to the best overall performance of the models, as well as speeding up their convergence process.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-domain versus Out-of-domain transfer learning for document layout analysis

De Nardin,

Zottin,

Piciarelli

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

In-domain versus out-of-domain transfer learning for document layout analysis

De Nardin,

Zottin,

Piciarelli

et al. 2024

IJDAR

View full text Add to dashboard Cite

Data availability is a big concern in the field of document analysis, especially when working on tasks that require a high degree of precision when it comes to the definition of the ground truths on which to train deep learning models. A notable example is represented by the task of document layout analysis in handwritten documents, which requires pixel-precise segmentation maps to highlight the different layout components of each document page. These segmentation maps are typically very time-consuming and require a high degree of domain knowledge to be defined, as they are intrinsically characterized by the content of the text. For this reason in the present work, we explore the effects of different initialization strategies for deep learning models employed for this type of task by relying on both in-domain and cross-domain datasets for their pre-training. To test the employed models we use two publicly available datasets with heterogeneous characteristics both regarding their structure as well as the languages of the contained documents. We show how a combination of cross-domain and in-domain transfer learning approaches leads to the best overall performance of the models, as well as speeding up their convergence process.

show abstract

A One-Shot Learning Approach to Document Layout Segmentation of Ancient Arabic Manuscripts

Cited by 2 publications

References 29 publications

In-domain versus Out-of-domain transfer learning for document layout analysis

In-domain versus Out-of-domain transfer learning for document layout analysis

In-domain versus out-of-domain transfer learning for document layout analysis

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