Journal | magazén
Journal issue | 5 | 2 | 2024
Research Article | NetLay: Layout Classification Dataset for Enhancing Layout Analysis
NetLay: Layout Classification Dataset for Enhancing Layout Analysis
Abstract
Within the domain of historical document image analysis, the process of identifying the spatial structure of a document image is an essential step in many document processing tasks, such as optical character recognition and information extraction. Advancements in layout analysis promise to enhance efficiency and accuracy using specialized models tailored to distinct layouts. We introduce NetLay, a new dataset for benchmarking layout classification algorithms for historical works. It consists of over 1,300 images of pages of printed Hebrew (or Hebrew‑character) books in a variety of styles, categorized into four different classes based on their layout (the number of text columns and regions). Ground truth was crafted manually at the page level. Furthermore, we conduct an in‑depth performance evaluation of various layout classification algorithms, which are based on deep‑learning models that learn to extract spatial features from images. We evaluate our algorithms on NetLay and achieve state‑of‑the‑art results on the task of layout classification for historical books.
Submitted: April 4, 2024 | Accepted: Sept. 23, 2024 | Published Dec. 17, 2024 | Language: en
Keywords Multi‑label classification • Historical document analysis • Convolutional neural networks • Layout analysis • Deep learning • Layout classification
Copyright © 2024 Sharva Gogawale, Luigi Bambaci, Berat Kurar-Barakat, Daria Vasyutinsky Shapira, Daniel Stökl Ben Ezra, Nachum Dershowitz. This is an open-access work distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction is permitted, provided that the original author(s) and the copyright owner(s) are credited and that the original publication is cited, in accordance with accepted academic practice. The license allows for commercial use. No use, distribution or reproduction is permitted which does not comply with these terms.
