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ICDAR 2019 CROHME + TFD: Competition on Recognition of Handwritten Mathematical Expressions and Typeset Formula Detection

Authors :
Utpal Garain
Harold Mouchère
Mahshad Mahdavi
Richard Zanibbi
Christian Viard-Gaudin
Department of Computer Science (RIT)
Rochester Institute of Technology
Laboratoire des Sciences du Numérique de Nantes (LS2N)
IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST)
Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
Image Perception Interaction (IPI)
Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)
Indian Statistical Institute [Kolkata]
Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Source :
15th IAPR International Conference on Document Analysis and Recognition (ICDAR 2019), 15th IAPR International Conference on Document Analysis and Recognition (ICDAR 2019), Sep 2019, Sydney, Australia, ICDAR
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; We summarize the tasks, protocol, and outcome for the 6th Competition on Recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare L A T E X strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. The strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D Institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME-still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.

Details

Language :
English
Database :
OpenAIRE
Journal :
15th IAPR International Conference on Document Analysis and Recognition (ICDAR 2019), 15th IAPR International Conference on Document Analysis and Recognition (ICDAR 2019), Sep 2019, Sydney, Australia, ICDAR
Accession number :
edsair.doi.dedup.....f5b3fdb77f6d181a3ace18654ad2c448