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#!/usr/bin/env python #!/usr/bin/env python
''' '''
SVM and KNearest digit recognition. SVM, Random forest and KNearest digit recognition.
Modified from the OpenCV example.
Sample loads a dataset of handwritten digits from '../data/digits.png'. Sample loads a dataset of handwritten digits from '../data/digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates Then it trains a Random Forest, SVM and KNearest classifiers on it and evaluates
their accuracy. their accuracy.
Following preprocessing is applied to the dataset: Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew()) - Moment-based image deskew (see deskew())
- Digit images are split into 4 10x10 cells and 16-bin - Digit images are split into 4 10x10 cells and 16-bin histogram of oriented
histogram of oriented gradients is computed for each gradients is computed for each cell
cell - Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
- Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
Or in the "simple setting":
- Only moment-based image deskew (see deskew())
[1] R. Arandjelovic, A. Zisserman [1] R. Arandjelovic, A. Zisserman
"Three things everyone should know to improve object retrieval" "Three things everyone should know to improve object retrieval"
http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
Usage: Usage:
digits.py digits.py
''' '''
# built-in modules # built-in modules