Recognition of Simple Handwritten Polynomials Using Segmentation with Fractional Calculus and Convolutional Neural Networks

Marques, Francisco das Chagas Fontenele; Araujo, Thelmo P. de; Sousa, José Vigno Moura; Costa, Nator Junior C.; Melo, Ricardo; Pinto, Alano; Saraiva, Aratã Andrade

doi:10.1109/bracis.2019.00051

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…Marques et al [85] presented an approach for the identification of handwritten polynomials through the utilization of CNN combined with fractional order Darwinian particle swarm optimization (FODPSO). The FODPSO method was applied for segmenting the input image, leveraging fractional derivatives to regulate the particle convergence rate.…”

Section: Other Techniquesmentioning

confidence: 99%

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

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Section: Other Techniquesmentioning

confidence: 99%

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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“…The learned features are then passed through the feedforward network to perform the classification, as shown in Figure 1. A number of MERs have been developed recently based on CNN [14,16,17,20].…”

Section: Symbol Recognitionmentioning

confidence: 99%

A Step Toward an Automatic Handwritten Homework Grading System for Mathematics

Chaowicharat

Dejdumrong

2023

ITC

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An automatic system that helps teachers and students verify the correctness of handwritten derivation in mathematics homework is proposed. The system acquires input image containing handwritten mathematical derivation. In our preliminary study, the system that comprises only mathematical expression recognition (MER) and computer algebra system (CAS) did not perform well due to high misrecognition rate. Therefore, our study focuses on fixing the misrecognized symbols by using symbols replacement and the surrounding information. If all the original mathematical expressions (MEs) in the derivation sequence are already equivalent, the derivation is marked as “correct”. Otherwise, the symbols with low recognition confidence will be replaced by other possible candidates to maximize the number of equivalent MEs in that derivation. If there is none of symbols replacement that makes every line equivalent, the derivation is marked as “incorrect”. The recursive expression tree comparison was applied to report the types of mistake for those problems marked as incorrect. Finally, the performance of the system was evaluated by the digitally generated dataset of 6,000 handwritten mathematical derivations. The results showed that the symbols replacement improve the F1-score of derivation step marking from 69.41 to 95.95 % for the addition/ subtraction dataset and from 61.45 to 89.95 % for the multiplication dataset when compared to the case of using raw recognized string without symbols replacement.

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“…Fractional calculus plays an essential role in depicting the complex nonlinear phenomena, 1–4 for example, mathematical physics, 5 fluid mechanics, 6 viscoelastic mechanics, 7 condensed matter physics, 8 and neural network 9 …”

Section: Introductionmentioning

confidence: 99%

“…Fractional calculus plays an essential role in depicting the complex nonlinear phenomena, [1][2][3][4] for example, mathematical physics, 5 fluid mechanics, 6 viscoelastic mechanics, 7 condensed matter physics, 8 and neural network. 9 Fractional differential equations appear more and more frequently for modeling of relevant systems in several fields of applied sciences. 10,11 In particular, the kinetic equations describe the continuity of motion of substance and are the basic equations of mathematical physics and natural science.…”

Section: Introductionmentioning

confidence: 99%