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dc.contributor.authorMirkazemy, Abolfazl-
dc.contributor.authorAdibi, Peyman-
dc.contributor.authorEhsani, Seyed Mohhamad Saied-
dc.contributor.authorDarvishy, Alireza-
dc.contributor.authorHutter, Hans-Peter-
dc.date.accessioned2023-09-22T13:15:30Z-
dc.date.available2023-09-22T13:15:30Z-
dc.date.issued2023-
dc.identifier.issn0893-6080de_CH
dc.identifier.issn1879-2782de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28766-
dc.description.abstractIn this paper, we propose a novel deep neural model for Mathematical Expression Recognition (MER). The proposed model uses encoder–decoder transformer architecture that is supported by additional pre/post-processing modules, to recognize the image of mathematical formula and convert it to a well-formed language. A novel pre-processing module based on domain prior knowledge is proposed to generate random pads around the formula’s image to create more efficient feature maps and keeps all the encoder neurons active during the training process. Also, a new post-processing module is developed which uses a sliding window to extract additional position-based information from the feature map, that is proved to be useful in the recognition process. The recurrent decoder module uses the combination of feature maps and the additional position-based information, which takes advantage of a soft attention mechanism, to extract the formula context into the LaTeX well-formed language. Finally, a novel Reinforcement Learning (RL) module processes the decoder output and tunes its results by sending proper feedbacks to the previous steps. The experimental results on im2latex-100k benchmark dataset indicate that each devised pre/post-processing as well as the RL refinement module has a positive effect on the performance of the proposed model. The results also demonstrate the higher accuracy of the proposed model compared to the state-of-the-art methods.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofNeural Networksde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMathematical expression recognitionde_CH
dc.subjectDeep learningde_CH
dc.subjectEncoder–decoder​ architecturede_CH
dc.subjectAttentionde_CH
dc.subjectScientific documents accessibilityde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleMathematical expression recognition using a new deep neural modelde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1016/j.neunet.2023.08.045de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end874de_CH
zhaw.pages.start865de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume167de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedHuman Information Interactionde_CH
zhaw.webfeedHuman-Centered Computingde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Mirkazemy, A., Adibi, P., Ehsani, S. M. S., Darvishy, A., & Hutter, H.-P. (2023). Mathematical expression recognition using a new deep neural model. Neural Networks, 167, 865–874. https://doi.org/10.1016/j.neunet.2023.08.045
Mirkazemy, A. et al. (2023) ‘Mathematical expression recognition using a new deep neural model’, Neural Networks, 167, pp. 865–874. Available at: https://doi.org/10.1016/j.neunet.2023.08.045.
A. Mirkazemy, P. Adibi, S. M. S. Ehsani, A. Darvishy, and H.-P. Hutter, “Mathematical expression recognition using a new deep neural model,” Neural Networks, vol. 167, pp. 865–874, 2023, doi: 10.1016/j.neunet.2023.08.045.
MIRKAZEMY, Abolfazl, Peyman ADIBI, Seyed Mohhamad Saied EHSANI, Alireza DARVISHY und Hans-Peter HUTTER, 2023. Mathematical expression recognition using a new deep neural model. Neural Networks. 2023. Bd. 167, S. 865–874. DOI 10.1016/j.neunet.2023.08.045
Mirkazemy, Abolfazl, Peyman Adibi, Seyed Mohhamad Saied Ehsani, Alireza Darvishy, and Hans-Peter Hutter. 2023. “Mathematical Expression Recognition Using a New Deep Neural Model.” Neural Networks 167: 865–74. https://doi.org/10.1016/j.neunet.2023.08.045.
Mirkazemy, Abolfazl, et al. “Mathematical Expression Recognition Using a New Deep Neural Model.” Neural Networks, vol. 167, 2023, pp. 865–74, https://doi.org/10.1016/j.neunet.2023.08.045.


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