Acceleration of Online Recognition of 2D Sequences Using Deep Bidirectional LSTM and Dynamic Programming

Zhelezniakov, Dmytro; Zaytsev, Viktor; Radyvonenko, Olga

doi:10.1007/978-3-030-20518-8_37

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…For example, Le and Nakagawa [25] proposed a system for recognizing online handwritten mathematical expressions using stochastic context-free grammar and the-Cocke-Younger-Kasami algorithm. Conversely, an end-to-end recognition approach using deep learning was proposed in [26], [27], [28], [29]. This challenge is expected to improve based on research progress.…”

Section: Handwriting-based Input Methodsmentioning

confidence: 99%

MathTOUCH Editor: Rich-text Editor for Math E-learning Using an Intelligent Math Input Interface

Shirai,

Nakahara,

Fukui

2023

Journal of Information Processing

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In this study, we presented MathTOUCH, a rich-text editor, to create mathematical documents. In mathematics e-learning with learning management systems, existing math input methods have heavy workloads; therefore, math notations are a long-standing challenge. Furthermore, when creating mathematical documents, such as writing reports and posting questions on the forum, users must enter both texts in a natural language input manner and mathematical expressions in a math input manner. Hence, users' workload increases. To address this issue, we developed the MathTOUCH editor to implement an intelligent math input interface that enables users to enter equations through predictive conversion from the colloquial-style text. A user study was conducted with 71 participants to evaluate the effectiveness of the proposed editor. The results indicate that users could enter equations with the MathTOUCH editor approximately 1.5 times faster than with a standard interface, and the participants also reported greater subjective satisfaction.

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Section: Handwriting-based Input Methodsmentioning

confidence: 99%

MathTOUCH Editor: Rich-text Editor for Math E-learning Using an Intelligent Math Input Interface

Shirai,

Nakahara,

Fukui

2023

Journal of Information Processing

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“…The second heuristic applies dynamic pruning to the search area depending upon detected character candidates. We used an additional dataset to train our system for both tasks [10].…”

Section: Participating Methodsmentioning

confidence: 99%

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

Mahdavi

Zanibbi

Mouchère

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

105

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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 L A T E X 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.

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“…This limits accuracy by underutilizing context, and by optimizing recognition for subtasks independently. Despite this, some commercial systems have used structural approaches in real systems and applications, such as in systems created by MyScript [17], Wiris [18], and Samsung [19].…”

Section: Structural Recognition Approachesmentioning

confidence: 99%

A survey on handwritten mathematical expression recognition: The rise of encoder-decoder and GNN models

Truong,

Nguyen,

Zanibbi

et al. 2024

Pattern Recognition

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Acceleration of Online Recognition of 2D Sequences Using Deep Bidirectional LSTM and Dynamic Programming

Cited by 18 publications

References 17 publications

MathTOUCH Editor: Rich-text Editor for Math E-learning Using an Intelligent Math Input Interface

MathTOUCH Editor: Rich-text Editor for Math E-learning Using an Intelligent Math Input Interface

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

A survey on handwritten mathematical expression recognition: The rise of encoder-decoder and GNN models

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