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Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot
BERT Question and Answer system meant and works well for only limited number of words summary like 1 to 2 paragraphs only. It canβt be able to answer well from understanding more than 10 pages of data. We can extend the BERT question and answer model to work as chatbot on large text. To accomplish the understanding of more than 10 pages of data, here we have used a specific approach of picking the data.4-simple-steps-in-Builiding-OCR
Optical character recognition (OCR) is process of classification of opti- cal patterns contained in a digital image. The character recognition is achieved through segmentation, feature extraction and classification. Keras Deep learning Network is used at here in recognising the Text characters and OpenCV is used in segmenting the text and Noise normalization.Semantic-Feature-generation-for-words
Building a Natural language Responsive system by Preprocessing(removing stop words , numbers, urls and stemmming ) raw text, generating features for words, Extracting Entities(Named Entity Recognition) based on specific application, Feeding the preprocessed and required text to Deep Learning Neural Network which can generate a responsive sentence for the given sentence.Nagakiran1
Receipt-Image-Classifier
Classifying the image either as Receipt or not, based on the considered Image patterns and the Extracted text associated with the considered Image.Crowd-Counting-CNN-density-based
Project is to count the number group of people in an image and to count number of persons associated with each group. Here two enhanced Convolutional models have used in building effective crowd counting architecture.Love Open Source and this site? Check out how you can help us