Gujarati Handwritten Character Recognition from Text Images

Pareek, Jyoti; Singhania, Dimple; Kumari, Rashmi; Purohit, Suchit

doi:10.1016/j.procs.2020.04.055

Cited by 34 publications

(4 citation statements)

References 2 publications

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“…e fitness function in the genetic algorithm is used to evaluate the fitness of individuals and distinguish the advantages and disadvantages of individuals in the population. e higher the fitness, the greater the probability of being inherited, and the better the clustering effect [15]. erefore, the selection of fitness function is very important, which directly affects the convergence speed of the genetic algorithm and the ability to find the optimal solution.…”

Section: Fitness Functionmentioning

confidence: 99%

[Retracted] Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

Yang

2022

Journal of Mathematics

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In order to overcome the problems of poor clustering effect and large error of text knowledge acquisition in traditional text knowledge acquisition methods, a new text knowledge acquisition method of collaborative product design based on genetic algorithm is proposed in this paper. The definition of collaborative product design text knowledge clustering is given. According to the operation process of the genetic algorithm, the chromosomes of clustered text are constructed and encoded and the initial population is obtained. The fitness function of clustering is constructed by the DB index evaluation method; the selection, crossover, and mutation operators in the genetic algorithm are determined; and the objective function of collaborative product design text knowledge clustering is constructed. After the text knowledge clustering is completed, the text knowledge data of collaborative product design are obtained in an all-around way by using the method of rough set and neural network. The experimental results show that compared with the traditional text knowledge acquisition methods, the clustering effect of the proposed method is better and the text knowledge error is reduced up to 0.02.

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Section: Fitness Functionmentioning

confidence: 99%

[Retracted] Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

Yang

2022

Journal of Mathematics

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“…It constructed a supervised classifier approach based on CNN and MLP to recognize handwritten Gujarati characters. Based on the experiment result, the accuracy generated using CNN is 97.21%, and the accuracy rate using MLP is 64.48% [7].…”

Section: Literature Reviewmentioning

confidence: 99%

How to Stepping up Characters Recognition using CNN Algorithm?

Khaled

2022

ijicom

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Character recognition is very important to understand ancient culture. Various papers proposed numerous to deal with handwritten character recognition. However, several traditional remain drawbacks because methods still rely on operations based on visual capabilities. Therefore, to deal with the issue, we propose a novel recognition model using a Convolutional Neural Network to produce an effective result. To build the model, we collect datasets, do preprocessing, training with several different parameters to get the highest accuracy results. Based on experiments, our proposed model can produce an accuracy quality with a value of 98.00%.

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“…If the two classes are linearly separable, the hyperplane that achieves the maximum margin between them is represented as in ( 9 (11) In this context, C represents the penalty parameter, and its value can enhance classification performance, while 𝛿 𝑖𝑗 denotes the Kronecker symbol. In this investigation, the kernel function utilized is radial based, as in (12):…”

Section: Support Vector Machinementioning

confidence: 99%

“…The DCNN model was found to be more suitable for all types of combined and compound data. The development of an artificial intelligence-based offline handwriting recognition system for Gujarati was studied in [12]. This study greatly contributed to data collection, collecting 10,000 images from 250 individuals of different ages and professions.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network for Lampung character recognition

Bintoro,

Zulkifli,

Fitriana

et al. 2024

Bulletin EEI

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Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.

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Gujarati Handwritten Character Recognition from Text Images

Cited by 34 publications

References 2 publications

[Retracted] Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

[Retracted] Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

How to Stepping up Characters Recognition using CNN Algorithm?

Deep convolutional neural network for Lampung character recognition

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