44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
def Levenshtein_Distance(str1, str2):
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matrix = [[ i + j for j in range(len(str2) + 1)] for i in range(len(str1) + 1)]
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for i in range(1, len(str1)+1):
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for j in range(1, len(str2)+1):
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if(str1[i-1] == str2[j-1]):
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d = 0
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else:
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d = 1
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matrix[i][j] = min(matrix[i-1][j]+1, matrix[i][j-1]+1, matrix[i-1][j-1]+d)
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return matrix[len(str1)][len(str2)]
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def cal_cer_ed(path_ours, tail='_rec'):
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path_gt='./GT/'
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N=66
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cer1=[]
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cer2=[]
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ed1=[]
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ed2=[]
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check=[0 for _ in range(N+1)]
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lis=[1,2,3,4,5,6,7,9,10,21,22,23,24,27,30,31,32,36,38,40,41,44,45,46,47,48,50,51,52,53] # DocTr (Setting 1)
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# lis=[1,9,10,12,19,20,21,22,23,24,30,31,32,34,35,36,37,38,39,40,44,45,46,47,49] # DewarpNet (Setting 2)
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for i in range(1,N):
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if i not in lis:
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continue
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gt=Image.open(path_gt+str(i)+'.png')
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img1=Image.open(path_ours+str(i)+'_1' + tail)
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img2=Image.open(path_ours+str(i)+'_2' + tail)
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content_gt=pytesseract.image_to_string(gt)
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content1=pytesseract.image_to_string(img1)
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content2=pytesseract.image_to_string(img2)
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l1=Levenshtein_Distance(content_gt,content1)
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l2=Levenshtein_Distance(content_gt,content2)
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ed1.append(l1)
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ed2.append(l2)
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cer1.append(l1/len(content_gt))
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cer2.append(l2/len(content_gt))
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check[i]=cer1[-1]
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print('CER: ', (np.mean(cer1)+np.mean(cer2)) / 2.)
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print('ED: ', (np.mean(ed1)+np.mean(ed2)) / 2.)
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def evalu(path_ours, tail):
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cal_cer_ed(path_ours, tail)
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