使用小波分析实现文字种类自动识别

文章目录

数据简介
开始实验
- 小波分解
- 得出结果
- 结果分析
- 误差分析

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.sizeleft=(width-400)/2top=(height-800)/2right=(width+400)/2bottom=(height+800)/2copped_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。
在这里插入图片描述

在这里插入图片描述

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]]=LLimage[LL.shape[0]:,:LL.shape[1]]=LHimage[:LL.shape[0],LL.shape[1]:]=HLimage[LL.shape[0]:,LL.shape[1]:]=HHreturn 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)=coeffs1coeffs2=pywt.dwt2(LL1,'db4')LL2,(LH2,HL2,HH2)=coeffs2image=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。
在这里插入图片描述

得出结果

标准流程。

from PIL import Image
import os
import numpy as np
import pywt
def fc(matrix):count=0for i in matrix:for j in i:count+=j**2return 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+creturn [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)=coeffs1coeffs2=pywt.dwt2(LL1,'db4')LL2,(LH2,HL2,HH2)=coeffs2lt1[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=0for j in metrics11[i]:d=[sum((np.array(j)-_)**2) for _ in mean11]if np.argmin(d)==i:count+=1print(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=0for j in metrics12[i]:d=[sum((np.array(j)-_)**2) for _ in mean12]if np.argmin(d)==i:count+=1print(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=0for 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+=1print(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=0for 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+=1print(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=0for j in metrics21[i]:d=[sum((np.array(j)-_)**2) for _ in mean21]if np.argmin(d)==i:count+=1print(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=0for j in metrics22[i]:d=[sum((np.array(j)-_)**2) for _ in mean22]if np.argmin(d)==i:count+=1print(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=0for 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+=1print(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=0for 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+=1print(zd[i+1],end="")print(" :{:06.2f}%".format(int(count/len(metrics22[i])*10000)/100),end=" ")
print()