Digit Recognition in real time through hand gestures has achieved great attention in machine learning and computer vision applications. This article focuses on identifying Bangla numerals in the air using hand motions. This research leads to the stairwell, allowing for more investigation in the same subject for various Bangla characters and even phrases. The major issue, however, is coping with the wide range of handwriting styles employed by various users. Many studies have been done on the identification of Bangla handwritten digits, but none has proven successful at recognizing Bangla digits in real time using hand gestures in the air. As a result, this article describes the creation of a Bangla digit recognition model that employs a Convolution Neural Network (CNN) to predict Bangla digits by observing hand movements in the air space. After a thorough examination, the suggested system attained a 98.37% accuracy on the BanglaLekha-Isolated dataset.

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In this AI-driven digital era, the pervasive nature of digital data is possible due to the widespread and cheap access to the Internet. Internet is continuously flourishing with data in many forms. Among them, textual data are a great source of information where people share their expressions in written format. Social media, blogs, online newspapers, government documents are some notable textual data sources. Information extraction from this enormous amount of data by manual inspection is time-consuming, cumbersome, and sometimes impossible. Natural Language Processing (NLP) is the computational domain for addressing those limitations by solving human language-related problems. Text summarization, Named entity recognition, Question answering are some of them where a common task for a machine is to generate coherent text. In such scenarios, the input is a sequence of text, and the output is also a sequence, but they differ in length. Sequence-to-Sequence (Seq2Seq) is an algorithmic approach to address that scenario by utilizing layers of recurrent units. However, the simple Seq2Seq model fails to capture the long-term relationship on the input sequence. Research shows that the attention mechanism guides the model to concentrate on specific inputs. Existing literature shows a lack of quality research on this text generation problem in the Bangla language, whereas many languages show excellent results. This work aims to develop such a system by incorporating attention to the Seq2Seq model and justifying its applicability by comparing it with baseline models. The model perplexity shows that the system can generate human-level readable text using a preprocessed dataset.

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