Evidence map›Paper›PMID 39640623›Full record

ArticleHeliyon2024

Deep transformer-based architecture for the recognition of mathematical equations from real-world math problems.

Tanjim Taharat Aurpa, Kazi Noshin Fariha, Kawser Hossain, Samiha Maisha Jeba, Md Shoaib Ahmed, Md Rawnak Saif Adib, Farhana Islam, Farzana Akter

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In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tanjim Taharat AurpaBangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh.
Kazi Noshin FarihaDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Bangladesh.
Kawser HossainDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Bangladesh.
Samiha Maisha JebaDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Bangladesh.
Md Shoaib AhmedDepartment of Computer Science, Boise State University, Boise, ID, USA.
Md Rawnak Saif AdibDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Bangladesh.
Farhana IslamBangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh.
Farzana AkterBangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying mathematical equations from real-world math problems presents a unique and challenging task within the field of Natural Language Processing (NLP). It has a wide range of applications in various areas, such as academics, digital content design, and the development of automatic or interactive learning systems. However, the accurate understanding of these equations still needs to be enhanced due to the intrinsic complexity of mathematical symbols and various structural formats. Additionally, the unique syntax, diverse symbols, and complex structure of mathematical equations present significant obstacles that traditional NLP methods and Optical Character Recognition (OCR) systems need help to overcome. In this research, we utilize deep transformer architecture to recognize mathematical equations, and we have utilized our novel dataset with 3433 distinct observations. This dataset, which we have collected to include a diverse range of mathematical equations, is used to predict six (

Indexed as

Bangla languageBangla text analysisEquation recognitionMathematical taskTransformer-based learning

Identifiers

PMID39640623
PMCPMC11620133

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.