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hi,

i am developing English Handwriting OCR. I use Zone based approach for feature extraction. Here I use 64 X 64 images.
So i have 64 features for one sample image. My SVM will be Multi-class SVM because i have 52 classes for both capital and simple letters.
Here is the format of feature vector.

Class A image1 0 : 0.222000 1 : 0.0250222 ..... 63 : 0.000052
Class A image2 (some float values) ....
Class A image400 (some float values)

likewise i have 400 images for both 52 classes. I have read as scaling data increase the accuracy of the prediction.
But i have few things to be make clear.

01. How can i scale these feature values ?

02. Is there any function to get the matching probability of each test feature vector in Open CV LibSVM?

(i search the Open CV 2.4.5 documentation, but i couldn't find this)

can anyone explain these? , and also with some few code lines if possible.

Thank you
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1 solution

hi all,

i found scaling data means , keep all training and testing data within a range such as 0 - 1 or -1 - +1. i will proceed with that. does anyone has any idea or suggestion, please comment it here. thank you
 
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