ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions

Mouchère, Harold; Viard-Gaudin, Christian; Zanibbi, Richard; Garain, Utpal

doi:10.1109/icfhr.2016.0116

Cited by 98 publications

(59 citation statements)

References 9 publications

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“…In order to evaluate the overall performance of offline handwritten mathematical expression recognition, for each formula in a dataset of handwritten mathematical expressions, paint it onto a bitmap image, then stroke extraction is applied, after that the detected strokes are passed to version 1.3 of MyScript Math recognizer, the winner of CROHME 2016 [33]. Aligning with the offline task in CROHME 2019 [10], expression level metrics computed from the symbol level label graphs of formulas are used to evaluate the proposed system.…”

Section: Performance Of Offline Recognitionmentioning

confidence: 99%

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Chan

2020

IEEE Access

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Offline handwritten mathematical expression recognition is often considered much harder than its online counterpart due to the absence of temporal information and the presence of background noise. In order to take advantage of the more developed techniques on online recognition and save resources, an oversegmentation approach is proposed to recover strokes from a textual bitmap image automatically. The proposed algorithm first break down the skeleton of a binarized image into junctions and segments, then segments are merged to form strokes, finally the ordering is determined by recursive projection and topological sort.Given a state-of-art online handwritten mathematical expression recognition system, the proposed procedure correctly recognized 58.22%, 65.65% and 65.05% of the offline formulas rendered from CROHME 2014, 2016 and 2019 respectively. Therefore, the effectiveness of stroke extraction to offline recognition is justified.

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Section: Performance Of Offline Recognitionmentioning

confidence: 99%

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Chan

2020

IEEE Access

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“…We validated the proposed model on CROHME 2014 [27] test set and CROHME 2016 [28] test set. The CROHME competition dataset is currently the most widely used public dataset for handwritten mathematical expression recognition.…”

Section: Training and Testing Detailsmentioning

confidence: 99%

Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

Zhang

Dai

2018

2018 24th International Conference on Pattern Recognition (ICPR)

134

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Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, recently we propose the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. In this study, we improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and restore the fine-grained details dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-theart methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.

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“…Our experiments are conducted on CROHME competition database [47,48], which is currently the most widely used dataset for HMER. The CROHME 2014 competition dataset consists of a training set of 8836 HMEs and a testing set of 986 HMEs.…”

Section: Dataset and Metricmentioning

confidence: 99%

Multi-modal Attention Network for Handwritten Mathematical Expression Recognition

Wang

Zhang

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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In this paper, we propose a novel stroke constrained attention network (SCAN) which treats stroke as the basic unit for encoder-decoder based online handwritten mathematical expression recognition (HMER). Unlike previous methods which use trace points or image pixels as basic units, SCAN makes full use of stroke-level information for better alignment and representation. The proposed SCAN can be adopted in both single-modal (online or offline) and multi-modal HMER. For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features. Using stroke-level features can explicitly group points or pixels belonging to the same stroke, therefore reduces the difficulty of symbol segmentation and recognition via the decoder with attention mechanism. For multi-modal HMER, other than fusing multi-modal information in decoder, SCAN can also fuse multi-modal information in encoder by utilizing the stroke based alignments between online and offline modalities. The encoder fusion is a better way for combining multi-modal information as it implements the information interaction one step before the decoder fusion so that the advantages of multiple modalities can be exploited earlier and more adequately when training the encoder-decoder model. Evaluated on a benchmark published by CROHME competition, the proposed SCAN achieves the state-of-the-art performance.

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ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions

Cited by 98 publications

References 9 publications

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

Multi-modal Attention Network for Handwritten Mathematical Expression Recognition

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