wxPython利用pytesser模块实现图片文字识别
Pytesser——OCR in Python using the Tesseract engine from Google
pytesser是谷歌OCR开源项目的一个模块，在python中导入这个模块即可将图片中的文字转换成文本。
链接：https://code.google.com/p/pytesser/
pytesser 调用了 tesseract。在python中调用pytesser模块，pytesser又用tesseract识别图片中的文字。
下面是整个过程的实现步骤：
1、首先要在code.google.com下载pytesser。https://code.google.com/p/pytesser/downloads/detail?name=pytesser_v0.0.1.zip
这个是免安装的，可以放在python安装文件夹的\Lib\site-packages\ 下直接使用
pytesser里包含了tesseract.exe和英语的数据包（默认只识别英文），还有一些示例图片，所以解压缩后即可使用。
可通过以下代码测试：
>>> from pytesser import *
>>> image = Image.open('fnord.tif') # Open image object using PIL
>>> print image_to_string(image) # Run tesseract.exe on image
fnord
>>> print image_file_to_string('fnord.tif')
fnordfrom pytesser import * #im = Image.open('fnord.tif') #im = Image.open('phototest.tif') #im = Image.open('eurotext.tif') im = Image.open('fonts_test.png') text = image_to_string(im) print text
注：该模块需要PIL库的支持。
2、解决识别率低的问题
可以增强图片的显示效果，或者将其转换为黑白的，这样可以使其识别率提升不少：
enhancer = ImageEnhance.Contrast(image1)
image2 = enhancer.enhance(4)可以再对image2调用 image_to_string识别
3、识别其他语言
tesseract是一个命令行下运行的程序，参数如下：
tesseract imagename outbase [-l lang] [-psm N] [configfile...]
imagename是输入的image的名字
outbase是输出的文本的名字，默认为outbase.txt
-l lang 是定义要识别的的语言，默认为英文
详见http://tesseract-ocr.googlecode.com/svn-history/r725/trunk/doc/tesseract.1.html
通过以下步骤可以识别其他语言：
（1）、下载其他语言数据包：
https://code.google.com/p/tesseract-ocr/downloads/list
将语言包放入pytesser的tessdata文件夹下
接下来修改pytesser.py的参数，下面是一个例子：
"""OCR in Python using the Tesseract engine from Google
http://code.google.com/p/pytesser/
by Michael J.T. O'Kelly
V 0.0.2, 5/26/08"""
import Image
import subprocess
import os
import StringIO
import util
import errors
tesseract_exe_name = 'dlltest' # Name of executable to be called at command line
scratch_image_name = "temp.bmp" # This file must be .bmp or other Tesseract-compatible format
scratch_text_name_root = "temp" # Leave out the .txt extension
_cleanup_scratch_flag = True # Temporary files cleaned up after OCR operation
_language = "" # Tesseract uses English if language is not given
_pagesegmode = "" # Tesseract uses fully automatic page segmentation if psm is not given (psm is available in v3.01)
_working_dir = os.getcwd()
def call_tesseract(input_filename, output_filename, language, pagesegmode):
"""Calls external tesseract.exe on input file (restrictions on types),
outputting output_filename+'txt'"""
current_dir = os.getcwd()
error_stream = StringIO.StringIO()
try:
os.chdir(_working_dir)
args = [tesseract_exe_name, input_filename, output_filename]
if len(language) > 0:
args.append("-l")
args.append(language)
if len(str(pagesegmode)) > 0:
args.append("-psm")
args.append(str(pagesegmode))
try:
proc = subprocess.Popen(args)
except (TypeError, AttributeError):
proc = subprocess.Popen(args, shell=True)
retcode = proc.wait()
if retcode!=0:
error_text = error_stream.getvalue()
errors.check_for_errors(error_stream_text = error_text)
finally: # Guarantee that we return to the original directory
error_stream.close()
os.chdir(current_dir)
def image_to_string(im, lang = _language, psm = _pagesegmode, cleanup = _cleanup_scratch_flag):
"""Converts im to file, applies tesseract, and fetches resulting text.
If cleanup=True, delete scratch files after operation."""
try:
util.image_to_scratch(im, scratch_image_name)
call_tesseract(scratch_image_name, scratch_text_name_root, lang, psm)
result = util.retrieve_result(scratch_text_name_root)
finally:
if cleanup:
util.perform_cleanup(scratch_image_name, scratch_text_name_root)
return result
def image_file_to_string(filename, lang = _language, psm = _pagesegmode, cleanup = _cleanup_scratch_flag, graceful_errors=True):
"""Applies tesseract to filename; or, if image is incompatible and graceful_errors=True,
converts to compatible format and then applies tesseract. Fetches resulting text.
If cleanup=True, delete scratch files after operation. Parameter lang specifies used language.
If lang is empty, English is used. Page segmentation mode parameter psm is available in Tesseract 3.01.
psm values are:
0 = Orientation and script detection (OSD) only.
1 = Automatic page segmentation with OSD.
2 = Automatic page segmentation, but no OSD, or OCR
3 = Fully automatic page segmentation, but no OSD. (Default)
4 = Assume a single column of text of variable sizes.
5 = Assume a single uniform block of vertically aligned text.
6 = Assume a single uniform block of text.
7 = Treat the image as a single text line.
8 = Treat the image as a single word.
9 = Treat the image as a single word in a circle.
10 = Treat the image as a single character."""
try:
try:
call_tesseract(filename, scratch_text_name_root, lang, psm)
result = util.retrieve_result(scratch_text_name_root)
except errors.Tesser_General_Exception:
if graceful_errors:
im = Image.open(filename)
result = image_to_string(im, cleanup)
else:
raise
finally:
if cleanup:
util.perform_cleanup(scratch_image_name, scratch_text_name_root)
return result
if __name__=='__main__':
im = Image.open('phototest.tif')
text = image_to_string(im, cleanup=False)
print text
text = image_to_string(im, psm=2, cleanup=False)
print text
try:
text = image_file_to_string('fnord.tif', graceful_errors=False)
except errors.Tesser_General_Exception, value:
print "fnord.tif is incompatible filetype. Try graceful_errors=True"
#print value
text = image_file_to_string('fnord.tif', graceful_errors=True, cleanup=False)
print "fnord.tif contents:", text
text = image_file_to_string('fonts_test.png', graceful_errors=True)
print text
text = image_file_to_string('fonts_test.png', lang="eng", psm=4, graceful_errors=True)
print text
这个是source里面提供的，其实若只要识别其他语言只要添加一个language参数就行了，下面是我的例子：
"""OCR in Python using the Tesseract engine from Google
http://code.google.com/p/pytesser/
by Michael J.T. O'Kelly
V 0.0.1, 3/10/07"""
import Image
import subprocess
import util
import errors
tesseract_exe_name = 'tesseract' # Name of executable to be called at command line
scratch_image_name = "temp.bmp" # This file must be .bmp or other Tesseract-compatible format
scratch_text_name_root = "temp" # Leave out the .txt extension
cleanup_scratch_flag = True # Temporary files cleaned up after OCR operation
def call_tesseract(input_filename, output_filename, language):
"""Calls external tesseract.exe on input file (restrictions on types),
outputting output_filename+'txt'"""
args = [tesseract_exe_name, input_filename, output_filename, "-l", language]
proc = subprocess.Popen(args)
retcode = proc.wait()
if retcode!=0:
errors.check_for_errors()
def image_to_string(im, cleanup = cleanup_scratch_flag, language = "eng"):
"""Converts im to file, applies tesseract, and fetches resulting text.
If cleanup=True, delete scratch files after operation."""
try:
util.image_to_scratch(im, scratch_image_name)
call_tesseract(scratch_image_name, scratch_text_name_root,language)
text = util.retrieve_text(scratch_text_name_root)
finally:
if cleanup:
util.perform_cleanup(scratch_image_name, scratch_text_name_root)
return text
def image_file_to_string(filename, cleanup = cleanup_scratch_flag, graceful_errors=True, language = "eng"):
"""Applies tesseract to filename; or, if image is incompatible and graceful_errors=True,
converts to compatible format and then applies tesseract. Fetches resulting text.
If cleanup=True, delete scratch files after operation."""
try:
try:
call_tesseract(filename, scratch_text_name_root, language)
text = util.retrieve_text(scratch_text_name_root)
except errors.Tesser_General_Exception:
if graceful_errors:
im = Image.open(filename)
text = image_to_string(im, cleanup)
else:
raise
finally:
if cleanup:
util.perform_cleanup(scratch_image_name, scratch_text_name_root)
return text
if __name__=='__main__':
im = Image.open('phototest.tif')
text = image_to_string(im)
print text
try:
text = image_file_to_string('fnord.tif', graceful_errors=False)
except errors.Tesser_General_Exception, value:
print "fnord.tif is incompatible filetype. Try graceful_errors=True"
print value
text = image_file_to_string('fnord.tif', graceful_errors=True)
print "fnord.tif contents:", text
text = image_file_to_string('fonts_test.png', graceful_errors=True)
print text
在调用image_to_string函数时，只要加上相应的language参数就可以了，如简体中文最后一个参数即为 chi_sim， 繁体中文chi_tra,
也就是下载的语言包的 XXX.traineddata 文件的名字XXX，如下载的中文包是 chi_sim.traineddata， 参数就是chi_sim :
text = image_to_string(self.im, language = 'chi_sim')至此，图片识别就完成了。
额外附加一句：有可能中文识别出来了，但是乱码，需要相应地将text转换为你所用的中文编码方式，如：
text.decode("utf8")就可以了