python implementation of SVM-based handwritten digit recognition function

In this article, we share the example of SVM handwritten digit recognition function of the specific code for your reference, the details are as follows

1、SVM handwritten number recognition

Identify the steps:

(1) Preparation of sample images.
(2) Image size standardization: standardize all image sizes to 8*8 size.
(3) Read the unknown sample image, extract image features, and generate an image feature set.
(4) The unknown test sample image feature set is fed into the SVM for testing and the results of the test are output.

Identification Code:

#!/usr/bin/env python
import numpy as np
import mlpy
import cv2
print 'loading ...'

def getnumc(fn):
 '''Return numeric features'''
 fnimg = (fn) #Read the image
 img=(fnimg,(8,8)) # Resize the image to 8*8
 alltz=[]
 for now_h in xrange(0,8):
 xtz=[] 
 for now_w in xrange(0,8):
  b = img[now_h,now_w,0]
  g = img[now_h,now_w,1]
  r = img[now_h,now_w,2]
  btz=255-b
  gtz=255-g
  rtz=255-r
  if btz>0 or gtz>0 or rtz>0:
  nowtz=1
  else:
  nowtz=0
  (nowtz) 
 alltz+=xtz
 return alltz
 
#Read sample numbers
x=[]
y=[]
for numi in xrange(1,10):
 for numij in xrange(1,5):
 fn='nums/'+str(numi)+'-'+str(numij)+'.png'
 (getnumc(fn))
 (numi)
 
x=(x)
y=(y)
svm = (svm_type='c_svc', kernel_type='poly',gamma=10)
(x, y)
print u"Training Sample Testing:"
print (x)
print u"Unknown Image Test:"
for iii in xrange (1,10):
 testfn= 'nums/test/'+str(iii)+'-'
 testx=[]
 (getnumc(testfn))
 print 
 print testfn+":",
 print (testx)

Sample:

Results:

This is the entire content of this article.