OCR | HMM | Language Model | Viterbi | Dynamic Programming | extract text from image | NLP | python
A python implantation of optical character recognition (OCR) with Hidden Markov Model (HMM) to extract text from an image.
This is a baseline implementation
The initial / transition probability parameters of the HMM are estimated from (Bigram) Language Model using MLE / add-k smoothing from a text corpus.
To compute the emission probability, a set of text images with ground truth labels trained with HMM at the very outset and later they are compared with observed images for a pixelwise match and from that the emission probability is computed using Binomial distribution with the r.v. as the number of pixel matches.
Once the parameters of the HMM are estimated, the Viterbi DP algorithm is used to find the max probable state sequence given the test image.
The bacpointers are used to backtrace / decode the hidden state (character) sequence / extract the text from image.
Heatmaps of tfor DP tables are shown.
Both clean and noisy test images are input to extract text with Viterbi decoding
#imageprocessing #imageprocessingpython #python #machinelearning #algorithm #optimization #nlp #languagemodels #dynamicprogramming #markov
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