Md. Mahbubur Rahman Tusher¹, Fahmid Al Farid^2,*, Md. Al-Hasan¹, Abu Saleh Musa Miah¹, Susmita Roy Rinky¹, Mehedi Hasan Jim¹, Sarina Mansor², Md. Abdur Rahim³, Hezerul Abdul Karim^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2633-2656, 2024, DOI:10.32604/cmc.2024.049296

Abstract 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… More >

Sardar Hasen Ali^*, Maiwan Bahjat Abdulrazzaq

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356

Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits,… More >