Liu Hongbiao stands as a seminal figure in the contemporary calligraphy scene,emblematic of the modern transformation of traditional calligraphic art.His artistic practice and theoretical framework embody a creative s...Liu Hongbiao stands as a seminal figure in the contemporary calligraphy scene,emblematic of the modern transformation of traditional calligraphic art.His artistic practice and theoretical framework embody a creative synthesis that is deeply rooted in tradition while actively engaging with contemporaneity.Guided by the principle of"antiquity in structure,modernity in form"and underpinned by a dialectical philosophy,Liu has redefined the aesthetic paradigm of wild cursive script.Demonstrating a profound mastery of classical brushwork,character structure,and rhythmic vitality,he has pioneered innovations in large-format wild cursive while adhering to orthodox principles.By integrating dialectical thinking into his practice,he has engendered a new expressive rhythm imbued with artistic tension and the spirit of the era.Furthermore,Liu amalgamates the cultural heritage of Jiangyou with his personal nostalgia to forge a regionally inflected calligraphy,thereby linking the art form to both historical memory and individual sentiment.Ultimately,his wild cursive calligraphy offers a compelling paradigm for understanding the potential trajectory of traditional calligraphy within a contemporary context.展开更多
This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers du...This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.展开更多
In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 charact...In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 characters for each chirography, were selected and analyzed by this system. The ＂Sumi＂ distribution of character was clarified from 12 directions and summarized into four parts of horizontal part, diagonal left part, vertical part and diagonal fight part. The character＇s contour line was converted to a signal data in order to calculate roundness index. The degree of character＇s radian was presented by roundness index. The smooth index was calculated at the same time. Additionally, width index, ＂Sumi＂ ratio, stability index also were calculated to contrast the features of each style. The main character points of four styles of ＂Kaisho＇, ＂Gyosho＂, ＂Sousho＂, ＂Hiragana＂ were extracted to compare each other, and provide a reference for learners. The learners could obtain the quantitative data to understand their work＇s characteristics. It can also be compared with other person＇s work by this system in order to improve learners＇ writing skill.展开更多
基金the project"Research on the Construction of Contemporary Literary and Artistic Aesthetics and Value Interpretation in Pingxiang",a support project of the Literary and Art Creation and Prosperity Project of the Federation of Literary and Art Circles of Pingxiang City.
文摘Liu Hongbiao stands as a seminal figure in the contemporary calligraphy scene,emblematic of the modern transformation of traditional calligraphic art.His artistic practice and theoretical framework embody a creative synthesis that is deeply rooted in tradition while actively engaging with contemporaneity.Guided by the principle of"antiquity in structure,modernity in form"and underpinned by a dialectical philosophy,Liu has redefined the aesthetic paradigm of wild cursive script.Demonstrating a profound mastery of classical brushwork,character structure,and rhythmic vitality,he has pioneered innovations in large-format wild cursive while adhering to orthodox principles.By integrating dialectical thinking into his practice,he has engendered a new expressive rhythm imbued with artistic tension and the spirit of the era.Furthermore,Liu amalgamates the cultural heritage of Jiangyou with his personal nostalgia to forge a regionally inflected calligraphy,thereby linking the art form to both historical memory and individual sentiment.Ultimately,his wild cursive calligraphy offers a compelling paradigm for understanding the potential trajectory of traditional calligraphy within a contemporary context.
文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.
文摘In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 characters for each chirography, were selected and analyzed by this system. The ＂Sumi＂ distribution of character was clarified from 12 directions and summarized into four parts of horizontal part, diagonal left part, vertical part and diagonal fight part. The character＇s contour line was converted to a signal data in order to calculate roundness index. The degree of character＇s radian was presented by roundness index. The smooth index was calculated at the same time. Additionally, width index, ＂Sumi＂ ratio, stability index also were calculated to contrast the features of each style. The main character points of four styles of ＂Kaisho＇, ＂Gyosho＂, ＂Sousho＂, ＂Hiragana＂ were extracted to compare each other, and provide a reference for learners. The learners could obtain the quantitative data to understand their work＇s characteristics. It can also be compared with other person＇s work by this system in order to improve learners＇ writing skill.
