Face detection and Attendance With SMS to parents using Deep learning Techniques
Python demo
Intent and entity chatbot
Intent and entity chatbot
Named Entity Recognition for Hindi-English Code-Mixed Social Media Text
Named Entity Recognition for Hindi-English Code-Mixed Social Media Text
Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a subtask of Information Extraction. The challenge of NER for tweets lies in the insufficient information available in a tweet. There has been a significant amount of work done related to entity extraction, but only for resource-rich languages and domains such as the newswire. Entity extraction is, in general, a challenging task for such an informal text, and code-mixed text further complicates the process with it’s unstructured and incomplete information. We propose experiments with different machine learning classification algorithms with word, character and lexical features. The algorithms we experimented with are Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF). In this paper, we present a corpus for NER in Hindi-English Code- Mixed along with extensive experiments on our machine learning models which achieved the best f1-score of 0.95 with both CRF and LSTM.
Python Demo
Multiple Face Recognition and Attendance Monitoring
Python demo
Hand Gesture Recognition using Opencv Python
The emerging of need of domestic robots in real world applications has raised enormous need for instinctive and interaction among human and computer interaction (HCI). In a few conditions where humans can’t contact hardware, the hand motion recognition framework more suitable. Hand gesture recognition is used on controlling robots, portable controllers, or application in smart home. Gesture recognition is mainly applicable for video conferencing, sign language recognition, distance learning and in some forensic identification. Based on fingers’ angles relative to the wrist, a finger angle prediction algorithm and a template matching metric are proposed. All possible gesture types of the captured image are first predicted, and then evaluated and compared to the template image to achieve the classification. Unlike other template matching methods relying highly on large training set, this scheme possesses high flexibility since it requires only one image as the template, and can classify gestures formed by different combinations of fingers.
Python Demo
Crop Yield Prediction using machine learning
Python demo