Hi,
I am curious about few things:
1. what are the samples you use for testing your classifier? merely one sample
is hard to do justice for its accuracy.
2. did you try to fine tune the hyper parameters for your svm?
3. you might be interested in this blog post, the author get a very impressive
result
http://peekaboo-vision.blogspot.de/2010/09/mnist-for-ever.html
regards,
Caleb
> On 24 May, 2014, at 12:36 am, klo uo <[email protected]> wrote:
>
> Replaying to myself...
>
> The cause for reported "problem" is that classifier samples have empty strips
> on both sides, so if I shrink my_array to 6 columns and add empty columns on
> both sides, I get expected value - zero.
>
> But still, results from this approach can't beat tesseract unfortunately for
> my samples.
>
>
>
>
>> On Thu, May 22, 2014 at 2:08 PM, klo uo <[email protected]> wrote:
>> Hi,
>>
>> I followed documentation for digit recognition, as I was hoping for
>> something better then OCR with minimal involvement from my side.
>>
>> Here is example: http://nbviewer.ipython.org/gist/klonuo/873868aaaa5d0e5a8aa0
>>
>> So I'm feeding the classifier with my own data compliant to format it
>> expects and get bogus result. I tried this for many digit samples, and
>> results are far than I naively expected.
>>
>> But, this trained dataset consists of 1800 digits, and by giving me results
>> that none match with digit I feed the predictor, my lucky guess is that I'm
>> giving wrong parameters or maybe using wrong estimator or else?
>>
>>
>> Here is the code from link, just in case:
>>
>> ========================================
>> >>> from pylab import *
>> >>> from sklearn import svm
>> >>> from sklearn import datasets
>> >>> digits = datasets.load_digits()
>> >>> clf = svm.SVC(gamma=0.001, C=100.)
>> >>> clf.fit(digits.data[:-1], digits.target[:-1])
>> >>> imshow(digits.data[-1].reshape(8, 8), interpolation='nearest',
>> >>> cmap='binary')
>> >>> clf.predict(digits.data[-1])
>> array([8])
>> >>> my_sample = array([0, 0, 1, 15, 15, 15, 15, 0, 0, 15, 15,
>> 13, 0, 12, 15, 15, 12, 15, 10, 0, 0, 0,
>> 15, 15, 15, 15, 0, 0, 0, 0, 15, 15, 15,
>> 15, 0, 0, 0, 0, 15, 15, 15, 15, 0, 0,
>> 0, 10, 15, 0, 15, 15, 0, 0, 4, 15, 8,
>> 0, 0, 7, 15, 15, 15, 1, 0, 0])
>> >>> imshow(my_sample.reshape(8, 8), interpolation='nearest', cmap='binary')
>> >>> clf.predict(my_sample) # expecting zero
>> array([1])
>> ========================================
>>
>>
>> Thanks,
>> klo
>
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