通过OCR从T恤的照片中提取代码

Question

我最近看到有人穿着T恤背面有一些Perl代码.我拍了一张照片并裁剪出代码:

接下来我尝试通过OCR从图像中提取代码,因此我安装了Tesseract OCR和Python绑定,pytesser.

Pytesser仅适用于TIFF图像,因此我在Gimp中转换了图像并输入以下代码(Ubuntu 9.10):

>>> from pytesser import *
>>> image = Image.open('code.tif')
>>> print image_to_string(image)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pytesser.py", line 30, in image_to_string
    util.image_to_scratch(im, scratch_image_name)
  File "util.py", line 7, in image_to_scratch
    im.save(scratch_image_name, dpi=(200,200))
  File "/usr/lib/python2.6/dist-packages/PIL/Image.py", line 1406, in save
    save_handler(self, fp, filename)
  File "/usr/lib/python2.6/dist-packages/PIL/BmpImagePlugin.py", line 197, in _save
    raise IOError("cannot write mode %s as BMP" % im.mode)
IOError: cannot write mode RGBA as BMP
>>> r,g,b,a = image.split()
>>> img = Image.merge("RGB", (r,g,b))
>>> print image_to_string(img)
Tesseract Open Source OCR Engine

     éi     _   l_` _ t  
  ’   ‘" fY`  
  {  W       IKQW
  ·  __·_  ‘ ·-»·      
       :W   Z  
  ··  I  A n   1   
           ;f        
     `    `      
`T     .' V   _ ‘  
I  {Z.; » ;,. , ;  y i-   4 : %:,,    
      `· »    V; ` ?    
‘,—·.    
H***li¥v·•·}I§¢   ` _  »¢is5#__·¤G$++}§;“»‘7·
  71   ’    Q  {  NH IQ
  ytéggygi {     ;g¤qg;gm·;,g(g,,3) {3;;+-
   § {Jf**$d$ }‘$p•¢L#d¤ Sc}
  »   i `  i A1:

这显然是来自OCR引擎的乱码.所以,我的问题是:

• 我需要做些什么才能从Tesseract中获得更好的OCR结果？
• 或者,是否有其他人有更好的运气以另一种方式从上面的图像中提取代码？

Answer 1

您可以输入比清理图像和安装OCR引擎更快的类型:

#!/usr/bin/perl
(my$d=q[AA                GTCAGTTCCT
  CGCTATGTA                 ACACACACCA
    TTTGTGAGT                ATGTAACATA
      CTCGCTGGC              TATGTCAGAC
        AGATTGATC          GATCGATAGA
          ATGATAGATC     GAACGAGTGA
            TAGATAGAGT GATAGATAGA
              GAGAGA GATAGAACGA
                TC GATAGAGAGA
                 TAGATAGACA G
               ATCGAGAGAC AGATA
             GAACGACAGA TAGATAGAT
           TGAGTGATAG    ACTGAGAGAT
         AGATAGATTG        ATAGATAGAT
       AGATAGATAG           ACTGATAGAT
     AGAGTGATAG             ATAGAATGAG
   AGATAGACAG               ACAGACAGAT
  AGATAGACAG               AGAGACAGAT
  TGATAGATAG             ATAGATAGAT
  TGATAGATAG           AATGATAGAT
   AGATTGAGTG        ACAGATCGAT
     AGAACCTTTCT   CAGTAACAGT
       CTTTCTCGC TGGCTTGCTT
         TCTAA CAACCTTACT
           G ACTGCCTTTC
           TGAGATAGAT CGA
         TAGATAGATA GACAGAC
       AGATAGATAG  ATAGAATGAC
     AGACAGAGAG      ACAGAATGAT
   CGAGAGACAG          ATAGATAGAT
  AGAATGATAG             ACAGATAGAC
  AGATAGATAG               ACAGACAGAT
  AGACAGACTG                 ATAGATAGAT
   AGATAGATAG                 AATGACAGAT
     CGATTGAATG               ACAGATAGAT
       CGACAGATAG             ATAGACAGAT
         AGAGTGATAG          ATTGATCGAC
           TGATTGATAG      ACTGATTGAT
             AGACAGATAG  AGTGACAGAT
               CGACAGA TAGATAGATA
                 GATA GATAGATAG
                    ATAGACAGA G
                  AGATAGATAG ACA
                GTCGCAAGTTC GCTCACA
])=~s/\s+//g;%a=map{chr $_=>$i++}65,84,67,
71;$p=join$;,keys%a;while($d=~/([$p]{4})/g
){next if$j++%96>=16;$c=0;for$d(0..3){$c+=
$a{substr($1,$d,1)}*(4**$d)}$perl.=chr $c}
             eval $perl;

编辑:错字.

Answer 2

预处理肯定会产生更可行的图像.

例如,这是Gimp"Levels","Gaussians差异"和图像上的"Levels"过滤器的结果.

Answer 3

只是RedDwight代码中的一些小错别字.

#!/usr/bin/perl
(my $d=q[AA                GTCAGTTCCT
  CGCTATGTA                 ACACACACCA
    TTTGTGAGT                ATGTAACATA
      CTCGCTGGC              TATGTCAGAC
        AGATTGATC          GATCGATAGA
          ATGATAGATC     GAACGAGTGA
            TAGATAGAGT GATAGATAGA
              GAGAGA GATAGAACGA
                TC GATAGAGAGA
                 TAGATAGACA G
               ATCGAGAGAC AGATA
             GAACGACAGA TAGATAGAT
           TGAGTGATAG    ACTGAGAGAT
         AGATAGATTG        ATAGATAGAT
       AGATAGATAG           ACTGATAGAT
     AGAGTGATAG             ATAGAATGAG
   AGATAGACAG               ACAGACAGAT
  AGATAGACAG               AGAGACAGAT
  TGATAGATAG             ATAGATAGAT
  TGATAGATAG           AATGATAGAT
   AGATTGAGTG        ACAGATCGAT
     AGAACCTTTCT   CAGTAACAGT
       CTTTCTCGC TGGCTTGCTT
         TCTAA CAACCTTACT
           G ACTGCCTTTC
           TGAGATAGAT CGA
         TAGATAGATA GACAGAC
       AGATAGATAG  ATAGAATGAC
     AGACAGAGAG      ACAGAATGAT
   CGAGAGACAG          ATAGATAGAT
  AGAATGATAG             ACAGATAGAC
  AGATAGATAG               ACAGACAGAT
  AGACAGACTG                 ATAGATAGAT
   AGATAGATAG                 AATGACAGAT
     CGATTGAATG               ACAGATAGAT
       CGACAGATAG             ATAGACAGAT
         AGAGTGATAG          ATTGATCGAC
           TGATTGATAG      ACTGATTGAT
             AGACAGATAG  AGTGACAGAT
               CGACAGA TAGATAGATA
                 GATA GATAGATAG
                    ATAGACAGA G
                  AGATAGATAG ACA
                GTCGCAAGTTC GCTCACA
])=~s/\s+//g;%a=map{chr $_=>$i++}65,84,67,
71;$p=join$;,keys%a;while($d=~/([$p]{4})/g
){next if$j++%96>=16;$c=0;for$d(0..3){$c+=
$a{substr($1,$d,1)}*(4**$d)}$perl.=chr $c}
             eval $perl;

执行时产生:

Just another genome hacker.

Answer 4

如果我是你,我会首先使用图片处理程序(例如GIMP)尽可能地清理图像,以便更容易理解OCR的输入.

如果可能,请创建仅黑白图像.

Answer 5

嗯,也许你需要处理图像,即通过一些过滤器,如"边缘检测",浮雕/雕刻或噪音过滤器......

Answer 6

良好的OCR受到自然语言冗余的强烈指导,以产生"可能是下一个角色"的子集.Perl代码没有给OCR提供这样的帮助.手动输入.