https://elcvia.cvc.uab.cat/ Electronic Journal on Computer Vision and Image Analysis CVC Press en-US ELCVIA Electronic Letters on Computer Vision and Image Analysis 1577-5097 Authors who publish with this journal agree to the following terms:<br /><ol type="a"><li>Authors retain copyright.</li><li>The texts published in this journal are – unless indicated otherwise – covered by the Creative Commons Spain <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">Attribution-NonComercial-NoDerivatives 4.0</a> licence. You may copy, distribute, transmit and adapt the work, provided you attribute it (authorship, journal name, publisher) in the manner specified by the author(s) or licensor(s). The full text of the licence can be consulted here: <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">http://creativecommons.org/licenses/by-nc-nd/4.0</a>.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol> https://elcvia.cvc.uab.cat/article/view/1833 <p>Extracting mathematical formulas from images of scientific documents and converting them into structured data for storage in a database is essential for their further use. However, recognizing and extracting math formulas automatically, rapidly, and effectively can be challenging. To handle this problem, we have proposed a system, with a deep learning architecture, that uses the formula combination features to train the YOLOv8 model. This system can detect and classify the formula inside and outside the text. Once extracted, we built a robust end-to-end math formula recognition system that automatically identifies and classifies math symbols, using the faster R-CNN object detection, then a Convolution Graphical Neural network (ConvGNN) to analyze the math formula layout, as the formula is better represented as a graph with complex relationships and object interdependency. ConvGNN can predict formula linkages without resorting to laborious feature engineering. Experimental results on the IBEM and CROHME 2019 datasets reveal that the proposed approach can accurately extract isolated formulas with mAP of 99.3\%, embedded formulas with mAP of 80.3%, detect symbols with mAP of 87.3%, and analyze formula layout with an accuracy of 92%. We also showed that our system is competitive with related work.</p> Afef Kacem Takwa Ben Aïcha Gader Kawther Khazri Ayeb Copyright (c) 2024 Afef Kacem https://creativecommons.org/licenses/by-nc-nd/4.0 2024-09-06 2024-09-06 23 2 10.5565/rev/elcvia.1833