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The purpose of this research was to see if Kinect sensor can recognize numeric and alphabetic characters written with the hand in the air. Kinect sensor can capture motion without the sensor device being attached to the user's body. The input screen has both modes of numerals and alphabet. The recognition rate was measured and the user wrote the numbers from zero to nine and the letters from A to Z twice. Alphabet recognition relied on Palm's Graffiti. The input numerals and alphabet were recognized by dynamic programming matching based on interstroke information. In addition, this system can perform the numeral operation, such as +, -, x, and /. Most people are not used to writing in the air and are unfamiliar with Kinect sensor, and it takes some time to master them both. First, the user needs to become accustomed to using the sensor. Average recognition rates of 95.0% and 98.9%, respectively, were obtained for numerical and alphabetical characters. [ABSTRACT FROM AUTHOR]

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework for automated English-to-Bangla translation due to a lack of a comprehensive dataset that encompasses the diverse vocabulary of the Bangla language. In this study, we have proposed an English-to-Bangla MT system using an encoder-decoder attention model using the CCMatrix corpus. Our method shows that this model can outperform traditional SMT and RBMT models with a Bilingual Evaluation Understudy (BLEU) score of 15.68 despite being constrained by the limited vocabulary of the corpus. We hypothesize that this model can be used successfully for state-of-the-art machine translation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques. [ABSTRACT FROM AUTHOR]