We're currently working on exciting new updates and features, including UI changes. Stay tuned!
In recent years, Machine Learning and Data Mining based research become prevalent and handwritten recognition is one of the hotcakes. Bangla handwritten word recognition and extraction acquired huge attention in many research sectors like Computer Vision, Image Processing, Machine Learning, and many others for a large field of applications. To tackle this challenging problem, a perfect segmentation and recognition method are described in this paper with a good percentage of accuracy. The main challenge was to introduce a sound segmentation system and merge multi-zoned characters. This paper proposes a multi-zoned character segmentation, and a merging method is also proposed, which can produce the handwritten term. Utilizing Convolutional Neural Network (CNN) for preparing 84% precision is accomplished for character level, and 82% precision is achieved in word level.
Human activity recognition (HAR) is a wide field of study which identifies a person's specific movement or behavior based on sensor data. Recognition of human behavior is the origin of many technologies, such as those concerned with personal biometric signatures, sports training, digital computing, security, health and fitness tracking, ambient-assisted living and management. Studying recognition of human activity shows that researchers are mostly interested in human everyday activities. HAR models input is the reading of the raw sensor data, and output is the prediction of the movement activities of the user. The HAR framework is becoming an evolving discipline in intelligent computing applications in the field of pervasive computing. In our study, we applied several machine learning algorithm along with some preprocessing techniques to identify which algorithm performs better in dataset acquired from the WISDM laboratory, which is available in public domain. The experiment shows that the highest accuracy is achieved in phone accelerometer data using Principal Component Analysis (PCA) with Random Forest (RF) than any other algorithm and preprocessing techniques in terms of human activity recognition. This experiment will help perform more work on the basis of implementing classification and preprocessing techniques to identify human activities.