A Methodological Study of Document Layout Analysis

Zhang, Chunhu; Ibrayim, Mayire; Hamdulla, Askar

doi:10.1109/vrhciai57205.2022.00009

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…These are characterized by various degrees of degradation, inconsistent conditions in the capture of the instances of datasets and large amounts of additions and corrections which are heavily intertwined with the main text. These characteristics make it nearly impossible to rely on the most popular techniques adopted for printed documents, which typically rely on bounding boxes to group together the different elements of the page layout [4].…”

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: Introductionmentioning

confidence: 99%

“…Precision × Recall Precision + Recall(4) each metric was calculated individually for each document class of the 2 target datasets. Furthermore, a macro average of the scores obtained for the different segmentation classes was performed, to ensure that each of them contributes equally to the final score.…”

mentioning

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