The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise t...The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.展开更多
There were mainly six types of formalization models found in the study for 95 city and county names in China’s Hunan province,namely,the environment in a place for the place,the wish of the nomenclator for the place,...There were mainly six types of formalization models found in the study for 95 city and county names in China’s Hunan province,namely,the environment in a place for the place,the wish of the nomenclator for the place,the relative position of a place for the place,the resident for the place,the legend for the place,and the function of a place for the place.In the six formalization models,environment in a place for the place was the most in number,forging 47 names.Besides,the wish of the nomenclator for the place and the relative position of a place for the place came the second,taking 20 names respectively.The cognitive operation participating in the formalization was primarily single metonymy with only a few complex metonymies.Metaphtonymy could be only noted in the model of the wish of the nomenclator for the place.It was notable that single metaphor was missing in the cognitive operations.展开更多
基金MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.
文摘There were mainly six types of formalization models found in the study for 95 city and county names in China’s Hunan province,namely,the environment in a place for the place,the wish of the nomenclator for the place,the relative position of a place for the place,the resident for the place,the legend for the place,and the function of a place for the place.In the six formalization models,environment in a place for the place was the most in number,forging 47 names.Besides,the wish of the nomenclator for the place and the relative position of a place for the place came the second,taking 20 names respectively.The cognitive operation participating in the formalization was primarily single metonymy with only a few complex metonymies.Metaphtonymy could be only noted in the model of the wish of the nomenclator for the place.It was notable that single metaphor was missing in the cognitive operations.