使用小波分析实现文字种类自动识别_业界新闻

发布时间:2024-07-19 01:58

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文章目录

数据简介
开始实验

数据简介

各找一篇中文，日文，韩文，英文，俄文较长的学术论文。将论文转化为JPG格式。拆分每张JPG生成更多小的JPG。最终获得很多5个不同语言的JPG并且自带标签。数据链接：提取码8848。

将PDF转化为JPG。

import aspose.words as aw for i in range(1,6):     doc=aw.Document(f"data/{i}/{i}.pdf")     for page in range(0,doc.page_count):         extractedPage=doc.extract_pages(page,1)         extractedPage.save(f"dataset/{i}/{page+1}.jpg")

确认所有JPG大小是否一样。结果为假。

from PIL import Image import os sizes=[] for i in range(1,6):     for filename in os.listdir(f"dataset/{i}"):         if filename.endswith(".jpg"):             with Image.open(os.path.join(f"dataset/{i}",filename)) as img:                 sizes.append(img.size) flag=True for i in sizes:     if i!=sizes[0]:         flag=False;break print(flag)

初步裁切JPG取正中间的400*800个像素点(因为所有JPG的大小都大于400*800)。

from PIL import Image import os sizes=[] for i in range(1,6):     for filename in os.listdir(f"dataset/{i}"):         if filename.endswith(".jpg"):             with Image.open(os.path.join(f"dataset/{i}",filename)) as img:                 width,height=img.size                 left=(width-400)/2                 top=(height-800)/2                 right=(width+400)/2                 bottom=(height+800)/2                 copped_img=img.crop((left,top,right,bottom))                 copped_img.save(f"dataset_new/{i}/{filename}")

拆分大小为400*800的JPG为32张100*100的JPG。

from PIL import Image import os sizes=[] for i in range(1,6):     for filename in os.listdir(f"dataset_new/{i}"):         if filename.endswith(".jpg"):             with Image.open(os.path.join(f"dataset_new/{i}",filename)) as img:                 for x in range(0,400,100):                     for y in range(0,800,100):                         box=(x,y,x+100,y+100)                         tile=img.crop(box)                         tile.save(f"dataset_last_temp/{i}/{filename[:-4]}"+f"_{x//100}{y//100}"+".jpg")

人为地手动删除一些没有文字地的JPG，保存在dataset_last中。

展示其中一些数据：从上往下依次是中、日、韩、英、俄。

开始实验

小波分解

为了方便展示结果，对LL2,LH2,HL2,HH2,LH1,HL1,HH1进行了裁剪。实际实验中没有进行裁剪。

from PIL import Image import os import numpy as np import pywt import matplotlib.pyplot as plt def fc(LL,LH,HL,HH,x):     LL=LL[:x,:x]     LH=LH[:x,:x]     HL=HL[:x,:x]     HH=HH[:x,:x]     image=np.zeros((LL.shape[0]+LH.shape[0],LL.shape[1]+HL.shape[1]))     image[:LL.shape[0],:LL.shape[1]]=LL     image[LL.shape[0]:,:LL.shape[1]]=LH     image[:LL.shape[0],LL.shape[1]:]=HL     image[LL.shape[0]:,LL.shape[1]:]=HH     return image for i in range(1,6):     for filename in os.listdir(f"dataset_last/{i}"):         if filename.endswith(".jpg"):             with Image.open(os.path.join(f"dataset_last/{i}",filename)) as img:                 img=img.convert('L')                 coeffs1=pywt.dwt2(img,'db4')                 LL1,(LH1,HL1,HH1)=coeffs1                 coeffs2=pywt.dwt2(LL1,'db4')                 LL2,(LH2,HL2,HH2)=coeffs2                 image=fc(fc(LL2,LH2,HL2,HH2,25),LH1,HL1,HH1,50)                 image=Image.fromarray(image.astype('uint8'))                 image.save(f"temp/{i}/{filename}")

展示其中一些结果：从上往下依次是中、日、韩、英、俄。

得出结果

标准流程。

from PIL import Image import os import numpy as np import pywt def fc(matrix):     count=0     for i in matrix:         for j in i:             count+=j**2     return count/(matrix.shape[0]*matrix.shape[1]) def metric1(LH,HL,HH):     return [fc(LH),fc(HL),fc(HH)] def metric2(LH,HL,HH):     x=metric1(LH,HL,HH)     a,b,c=x[0],x[1],x[2]     d=a+b+c     return [a/d,b/d,c/d] lt1=[[] for _ in range(5)] lt2=[[] for _ in range(5)] for i in range(1,6):     for filename in os.listdir(f"dataset_last/{i}"):         if filename.endswith(".jpg"):             with Image.open(os.path.join(f"dataset_last/{i}",filename)) as img:                 img=img.convert('L')                 coeffs1=pywt.dwt2(img,'db4')                 LL1,(LH1,HL1,HH1)=coeffs1                 coeffs2=pywt.dwt2(LL1,'db4')                 LL2,(LH2,HL2,HH2)=coeffs2                 lt1[i-1].append([LH1,HL1,HH1])                 lt2[i-1].append([LH2,HL2,HH2]) metrics11=[[metric1(_[0],_[1],_[2]) for _ in lt1[i]] for i in range(5)] metrics12=[[metric2(_[0],_[1],_[2]) for _ in lt1[i]] for i in range(5)] mean11=[np.mean(metrics11[i],axis=0) for i in range(5)] mean12=[np.mean(metrics12[i],axis=0) for i in range(5)] var11=[np.var(metrics11[i],axis=0) for i in range(5)] var12=[np.var(metrics12[i],axis=0) for i in range(5)] metrics21=[[metric1(_[0],_[1],_[2]) for _ in lt2[i]] for i in range(5)] metrics22=[[metric2(_[0],_[1],_[2]) for _ in lt2[i]] for i in range(5)] mean21=[np.mean(metrics21[i],axis=0) for i in range(5)] mean22=[np.mean(metrics22[i],axis=0) for i in range(5)] var21=[np.var(metrics21[i],axis=0) for i in range(5)] var22=[np.var(metrics22[i],axis=0) for i in range(5)] zd={1:"中文",2:"日文",3:"韩文",4:"英文",5:"俄文"} print(f"{'1次分解-DEMW:':<14}",end=" ") for i in range(5):     count=0     for j in metrics11[i]:         d=[sum((np.array(j)-_)**2) for _ in mean11]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics11[i])*10000)/100),end=" ") print() print(f"{'1次分解-DPMW:':<14}",end=" ") for i in range(5):     count=0     for j in metrics12[i]:         d=[sum((np.array(j)-_)**2) for _ in mean12]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics12[i])*10000)/100),end=" ") print() print(f"{'1次分解-DEMWV:':<14}",end=" ") for i in range(5):     count=0     for j in metrics11[i]:         d=[sum(((np.array(j)-mean11[k])**2)/(var11[k]**2)) for k in range(5)]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics11[i])*10000)/100),end=" ") print() print(f"{'1次分解-DPMWV:':<14}",end=" ") for i in range(5):     count=0     for j in metrics12[i]:         d=[sum(((np.array(j)-mean12[k])**2)/(var12[k]**2)) for k in range(5)]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics12[i])*10000)/100),end=" ") print() print(f"{'2次分解-DEMW:':<14}",end=" ") for i in range(5):     count=0     for j in metrics21[i]:         d=[sum((np.array(j)-_)**2) for _ in mean21]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics21[i])*10000)/100),end=" ") print() print(f"{'2次分解-DPMW:':<14}",end=" ") for i in range(5):     count=0     for j in metrics22[i]:         d=[sum((np.array(j)-_)**2) for _ in mean22]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics22[i])*10000)/100),end=" ") print() print(f"{'2次分解-DEMWV:':<14}",end=" ") for i in range(5):     count=0     for j in metrics21[i]:         d=[sum(((np.array(j)-mean21[k])**2)/(var21[k]**2)) for k in range(5)]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics21[i])*10000)/100),end=" ") print() print(f"{'2次分解-DPMWV:':<14}",end=" ") for i in range(5):     count=0     for j in metrics22[i]:         d=[sum(((np.array(j)-mean22[k])**2)/(var22[k]**2)) for k in range(5)]         if np.argmin(d)==i:             count+=1     print(zd[i+1],end="")     print(" :{:06.2f}%".format(int(count/len(metrics22[i])*10000)/100),end=" ") print()