Experimental word classifier based on dynamic time warping
Pleiad helps you identify words in an image. Use it when you dont need extensive character recognition and/or dont have much data, and need to identify few pre planned words only.
Pleiad doesn't need extensive training data, just a single image for each class (few more for better result) is all you need.
- Install R (and python)
install.packages('dtw')in R shell
pip install -r requirements
Crop words from image and stretch to a fixed size (one size per classifier)
Wordobjects from word images
from pleiad import pleaid word = pleiad.Word(image, "climb")
- Train classifier from a list of
classifier = pleiad.PleiadClassifier(image.shape) classifier.train(word_list)
- Save for future use
Pleiad works by treating the outer outline of each image of word as a time series and predicting by using the dynamic time warping distance between the series.
Copyright (c) 2014 Abhinav Tushar