Research On Font Substitution And Font Recognition Methods In Electronic Publications

Font plays an important role in reading and understanding of document content,also determines the visual effect of publications.With the development of electronic publishing technology,the number of fonts are increasing and the layout effect is increasingly rich.This brings great challenges to the reading platforms.On the ordinary reading platform,the number of fonts installed is limited because of the capacity of the system and the copyright of the font.When the specified font is not supported by the platform,the content is either blank or replaced with other fonts.There is no ideal font substitution method so far.When the publication is displayed with replaced font,the actual layout effect is often different from the expected effect,which seriously affects the reader's reading experience.Therefore,it is urgent to study the substitution algorithm of the font.On the other hand,when the publication is presented,it is difficult to tell the exact font being used from the tens to hundreds fonts.In the examination of the publication quality,it is necessary to judge whether the font is correct by manual method,and it often takes a lot of manpower and time,and the result is usually not accurate enough.Therefore,it is necessary to study the method for font recognition.In addition,accurate font recognition is also helpful to substitute font effectively.In view of the above requirements,this study investigated the previous Chinese and English font substitution methods as well as the existing font recognition results,the advantages and disadvantages of the previous methods are explored.Targeting at the font substitution of electronic publications,a font substitution method based on image features is proposed.Targeting at the font recognition,a deep learning based method is proposed.The experiment shows that the font substitution method proposed in this paper is more consistent with the human eye perception after the replacement,and the font recognition method proposed in this paper can achieve better accuracy in either smaller scale or larger scale font sets,which is better than the existing methods.The main research work in this paper is as follows:1)research the existing font replacement methods and font recognition methods,and analyze their advantages and disadvantages.2)put forward a font replacement method based on human vision.From many kinds of image features,it was found that the grayscale feature and texture feature are sensitive to fonts.On the basis of expert scoring,the multiple regression prediction model is constructed to determine the weight of different features according to the font vision in human eyes.The model is then used to measure the similarity between different fonts,and thus the substitution scheme is obtained at last.3)a font recognition method based on the deep convolution neural network is proposed.The GoogLeNet network model is used to simulate the human brain,and the high dimensional abstract features of the font image are learned independently.Finally,the trained model is used for the font recognition.4)design experiments to verify the effectiveness of font substitution and font recognition.The main innovations of this paper are as follows:1)the font substitution method is more close to the visual perception of the person than the existing method in the visual effect after the font replacement.It can keep the layout effect in maximum level and bring the reader a good reading experience.2)the font recognition method for single character is proposed in this paper.It can not only achieve a good recognition result on a small size of the common font set,but also achieve a good recognition result on a large scale font set,and the effect is better than the existing methods.In addition,the font recognition for single character can solve the problem of mixed font recognition,which is also a weakness in previous studies.The research results of this paper have important application value in optimizing the layout of electronic publications,improving readers' reading experience,and improving the automatic testing ability for electronic publications.