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.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:':'1次分解-DPMW:':'1次分解-DEMWV:':'1次分解-DPMWV:':'2次分解-DEMW:':'2次分解-DPMW:':'2次分解-DEMWV:':'2次分解-DPMWV:':