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This repository features projects on Part-of-Speech Tagging and Optical Character Recognition (OCR). For POS tagging, we use Bayes Nets and the Viterbi Algorithm to label words in sentences. Our OCR project uses emission probabilities and Hidden Markov Models (HMMs) to accurately read text from images.
An implementation of a language part-of-speech (POS) tagger using Hidden Markov Models. Basically, it takes English text as input and tries to tag each word as a noun, verb, adjective, etc. based on the input's sequence.
We are given 2 different problems to solve. 1. Isolated spoken digit recognition 2. Telugu Handwritten character recognition Both these datasets were given as a time series. 2 different methods were used to solve each of the problem: 1. Dynamic Time Warping 2. Hidden Markov Models
利用传统方法(N-gram,HMM等)、神经网络方法(CNN,LSTM等)和预训练方法(Bert等)的中文分词任务实现【The word segmentation task is realized by using traditional methods (n-gram, HMM, etc.), neural network methods (CNN, LSTM, etc.) and pre training methods (Bert, etc.)】