A neural framework for online recognition of handwritten Kanji characters

Grebowiec, Malgorzata; Protasiewicz, Jarosław

doi:10.15439/2018f140

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Based on its scope and aspects, writing can indeed be reviewed and studied from various aspects. [16,17] reveal that writing involves activities including (1) processing ideas, (2) structuring sentences, (3) developing paragraphs and (4) developing essays in certain types of discourse.…”

Section: Cohesion Tool As An Element Of Textmentioning

confidence: 99%

Text News in Japanese: The Study of the Relationship Between Elements of Text

Sambeka¹,

Sumampouw²,

Lerrich³

2023

Proceedings of the Unima International Conference on Social Sciences and Humanities (UNICSSH 2022)

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The Japanese news text is interesting to study to find out and describe the relationship between the elements that make up a coherent and logical text. This is also what underlies the author to examine the relationship between the elements that make up news texts in Japanese, which aims to explain and describe the relationship between the elements that make up news texts in Japanese. The research data were analyzed using content analysis with the results of the research (1) British English text as shown in paragraphs (1), ( 2) and (3); news writers only use the name of the institution, the name of the area, and the name of the person. References are not used, (2) the word seifu is substituted by sushou'prime minister'. Nichi'the 19th' is substituted by jitsu'on the same day', the word chiji'governor' is substituted by osakafu'Osaka government', (3) there are the sentences Tokyo and Hyogo ni tsuite no yosei o kanto. The conclusion in this study is that the specificity of the news text in Japanese is a reference that refers to the name of a person, but the name of the person, the name of the object, and the name of the institution are not used.

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Section: Cohesion Tool As An Element Of Textmentioning

confidence: 99%

Text News in Japanese: The Study of the Relationship Between Elements of Text

Sambeka¹,

Sumampouw²,

Lerrich³

2023

Proceedings of the Unima International Conference on Social Sciences and Humanities (UNICSSH 2022)

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“…4-Nearest Neighbour Baseline Model used to recognize MNIST, Kuzushiji-MNIST and Kuzushiji-49 dataset [3]. A CNN architecture for online detection of handwritten Kanji characters achieved accuracy up to 89% [5]. In the field of Japanese character recognition, previous work yielded an accuracy of accuracy rate of 97% by using fully Convolutional Neural Network (CNN) based filter [6].…”

Section: Related Workmentioning

confidence: 99%

Japanese Historical Character Recognition using Deep Convolutional Neural Network (DCNN) with DropBlock Regularization

Saini¹,

Verma²

2019

IJRTE

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Abstract: The history of a country always helps in the development of making modern society. Japanese classical handwritten/literature dataset Kuzushiji-MNIST and Kuzushiji-49 recently introduced in public domain by Center of Open Data in the Humanities (CODH). The availability of the datasets invite the researchers in different domains. The hidden treasure of information in these datasets resolved and worked by many researchers. The research on these datasets can be helpful to explore and analyze the detail of Japanese era/literature, which ultimately helps in the development of modern society. In this study, Deep Convolutional Neural Network (DCNN) withDropBlock regularization is used to recognize the hiragana characters in Japanese historical script.

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“…Some examples are the providing of the Lampung handwritten character dataset [1], historical handwritten digit documents of church records by priests in Sweden [2], and Arabic handwriting from historical manuscripts [3]. The methods and approaches in HWCR have also been applied for various scripts, for instance the Kurdish Text Classification of Sorani dialect [4], Slavic Historical Documents containing Glagolitic and Cyrillic character [5], printed Arabic [6], handwritten Bangla characters from India [7], handwritten Kanji characters [8], offline handwritten Chinese characters [9] and so on. The use of PCA specifically for handwritten character recognition is quite difficult to be found.…”

Section: Introductionmentioning

confidence: 99%

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

Junaidi

Trianingsih

Iqbal

2020

khif

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Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.

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A neural framework for online recognition of handwritten Kanji characters

Cited by 5 publications

References 6 publications

Text News in Japanese: The Study of the Relationship Between Elements of Text

Text News in Japanese: The Study of the Relationship Between Elements of Text

Japanese Historical Character Recognition using Deep Convolutional Neural Network (DCNN) with DropBlock Regularization

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

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