Tesseract	ocr	pdf	to	text	python
                                                                                                               	
  Ocr	image	to	text	in	python.	How	long	does	it	take	to	ocr	a	pdf.	How	to	convert	pdf	to	text	ocr.	How	to	ocr	pdf	with	tesseract.	Can	you	ocr	a	pdf.	
  Improve	the	article.	Save	the	article	as	an	article.	Python	is	widely	used	for	data	analysis,	but	the	data	may	not	always	be	in	the	right	format.	In	such	cases,	we	convert	this	format	(e.g.	PDF	or	JPG,	etc.)	to	a	text	format	for	better	analysis	of	the	data.	Python	provides	many	libraries	to	accomplish	this	task.	There	are	several	ways	to	do	this,	including
  using	libraries	like	PyPDF2	in	Python.	The	main	disadvantage	of	using	these	libraries	is	the	encoding	scheme.	PDF	documents	can	have	various	encodings	including	UTF-8,	ASCII,	Unicode,	etc.	Therefore,	converting	PDF	to	text	may	result	in	data	loss	due	to	the	encoding	scheme.	Let's	see	how	to	read	the	entire	content	of	a	PDF	file	and	save	it	to	a
  Word	document	using	OCR.	We	need	to	first	convert	PDF	pages	to	images	and	then	use	OCR	(Optical	Character	Recognition)	to	read	the	content	of	the	image	and	save	it	in	a	text	file.	Required	installation:	pip3	install	PIL	pip3	install	pytesseract	pip3	install	pdf2image	sudo	apt-get	install	tesseract-ocr	The	program	consists	of	the	following	two	parts:
  Part	1	deals	with	the	conversion	of	PDF	to	image	files.	Each	PDF	page	is	saved	as	an	image	file.	Names	of	saved	images:	PDF	Page	1	->	Page_1.jpg	PDF	Page	2	->	Page_2.jpg	PDF	Page	3	->	Page_3.jpg	….	PDF	page	n	->	page_n.jpg.	Part	2	is	about	OCR	text	from	image	files	and	storing	it	in	a	text	file.	Here	we	process	images	and	convert	them	to	text.
  Once	we've	got	the	text	as	a	string	variable,	we	can	do	whatever	we	want	with	it.	For	example,	in	many	PDF	files,	if	a	line	is	full,	but	a	specific	word	cannot	be	written	entirely	on	the	same	line,	a	hyphen	(â-â)	is	added	and	the	word	continues	on	the	next	line.	Example:	"This	is	sample	text,	but	this	specific	word	cannot	be	written	on	the	same	line.	Now
  basic	pre-processing	is	performed	on	such	words	to	convert	the	hyphen	and	newline	into	a	whole	word.	After	pre-processing	is	completethis	text	is	saved	in	a	separate	text	file.	For	source	PDFs	used	in	the	code,	click	d.pdf.	Here	is	the	implementation:	CR\Tesseract	tesseract.exe"	)	path	to_poppler_exe	=	Path(r"C:\.....")	out_directory	=
  Path(r"~\Desktop").expanduser()else:	out_directory	=	Path	("	~")	.expanduser()	PDF_file	=	Path(r"d.pdf")image_file_list	=	[]text_file	=	out_directory	/	Path("out_text.txt")def	main():	with	TemporaryDirectory()	as	tempdir:	if	platform	.system	()	=	=	"Windows":	pdf_pages	=	convert_from_path(PDF_file,	500,	poppler_path=path_to_poppler_exe)	else:
  pdf_pages	=	convert_from_path(PDF_file,	500)	for	page_list,	page	in	list(pdf_pages,	start=1):	file_temp_name	=	f\	0number	}	.jpg"	page.save(filename,	"JPEG")	image_file_list.append(filename)	from	open(text_file,	"a")	as	output_file:	image_file	m	in	image_file	e_list:	text	=	str(((pytesseract.image_to_string(Image	.open	(image_file)))))	text	=
  text.replace("-",	"")	output_file.write(text)if	__name__	==	"__main__	":	main()Output:	input	PDF	file:	output	text	file:	as	we	see,	that	the	PDF	pages	have	been	converted	to	images.	The	images	were	then	read	and	the	content	written	to	a	text	file.	Advantages	of	this	method:	No	text	conversion	due	to	loss	of	data	encoding	scheme.	Even	handwritten
  content	in	a	PDF	can	be	recognized	with	OCR.	It	is	also	possible	to	recognize	only	certain	PDF	pages.	text	as	a	variable	so	that	any	necessary	preprocessing	can	be	done.	Disadvantages	of	this	method	include:	Auxiliary	storage	is	used	to	store	images	on	the	local	system.	Although	these	pictures	are	tiny.	100%	accuracy	cannot	be	guaranteed	when
  using	OCR.	Computerized	PDF	document	providedwith	very	high	accuracy.	Handwritten	PDFs	are	still	recognized,	but	the	accuracy	depends	on	various	factors	such	as	handwriting,	page	color,	etc.	This	post	explains	how	to	extract	text	from	a	PDF	using	Python.	Extracting	text	from	the	PDFs	below	requires	two	Python	modules.	A	prerequisite	for
  using	the	pytesseract	pytesseract	module	is	the	tesseract	executable.	Let's	set	up	tesseract	for	Windows.	1.	Download	the	tesseract	executable	from	this	link.	2.	Install	the	downloaded	tesseract	executable.	A	prerequisite	for	using	the	pdf2image	module	pdf2image	is	the	PDF	rendering	library	Poppler.	Let's	set	up	Poppler	for	Windows.	1.	Download
  Poppler	from	this	link.	2.	Extract	the	downloaded	binary	file	and	place	the	extracted	folder	in	the	C:\Program	Files\	folder.	Extracting	text	from	PDF	Extracting	text	from	PDF	is	a	two-step	process,	first	the	PDF	needs	to	be	converted	to	images	using	pdf2image	and	then	the	images	need	to	be	converted	to	strings	using	pytesseract.	1.	Install	the
  required	modules.	pip	install	Pillow	pip	install	pdf2image	pip	install	pytesseract	2.	Import	the	required	modules	and	functions.	import	OS	from	PIL	import	image	import	pytesseract	from	pdf2image	import	convert_from_path	3.	Define	the	path	to	the	Poppler	executable	and	tesseract_cmd.	poppler_path	=	r'C:\Program	Files\poppler-0.68.0\bin'	#
  Replace	with	installation	location	pytesseract.pytesseract.tesseract_cmd	=	r'C:\Program	Files\Tesseract-OCR\tesseract'	#	Replace	with	installation	location	4.	Enter	the	path	to	a	PDF	file.	pdf_path	=	"sample.pdf"	#	Change	the	PDF	file	path	5.	Convert	the	PDF	file	to	images	using	the	convert_from_path	function.	images	=
  convert_from_path(pdf_path=pdf_path,	poppler_path=poppler_path)	6.	Preview	the	PDF	pages	and	save	each	page	as	a	PNG	image.	to	count	img	in	enumerate(images):	img_name	=	f"page_{count}.png"	img.save(img_name,	"PNG")	7.	After	successful	execution,	you	should	see	an	image	of	each	PDF	page	in	your	current	working	directory.	8.	List	all
  the	PNG	files	created	in	the	last	step.=	[f	for	f	in	os.listdir(".")	if.endswith(.png")]	9.	Extract	text	from	images	using	the	pytesseract.image_to_string	method.	for	png_file	in	png_files:	extracted_text	=	pytesseract.image_to_string(Image.open(png_file	))	print(extracted_text)	10.	Complete	code	snippet	for	extracting	text	from	PDF	files	#	Import	modules
  import	OS	from	PIL	import	image	import	pytesseract	from	pdf2image	import	convert_from_path	#	Define	paths	poppler_path	=	r'C:\Program	Files\poppler-0.68.0	\bin'	pytesseract.pytesseract.tesseract_cmd	=	r'C:\Program	Files	\	Tesseract-OCR\tesseract'	pdf_path	=	"sample.pdf"	#	Save	PDF	pages	as	images	images	=
  convert_from_path(pdf_path=pdf_path,	poppler_path=poppler_path);	img	in	enumerate(images):	img_name	=	f"page_{count}	.	png"	img.save(img_name,	"png")	#	Extract	text	png_files	=	[f	for	f	in	os.listdir(".")	if	f.endswith(.png")]	for	png_file	in	png_files:	extracted_text	=	pytesseract	.	image_to_string(Image.open(png_file))	print(extracted_text)	I
  have	scanned	a	PDF	file	and	am	trying	to	extract	the	text	from	it.	I	tried	using	pypdfocr	to	do	the	detection	but	got	the	error	"could	not	find	ghostscript	in	normal	location".	After	searching	I	found	this	solution.	When	combining	ghostscript	with	pypdfocr	on	a	windows	platform,	I	tried	downloading	the	ghostscript	and	putting	it	in	an	environment
  variable,	but	it	still	has	the	same	error.	How	can	I	search	for	text	in	a	scanned	PDF	using	python?	Thank	you.	Edit:	Here	is	my	sample	code:	:	self.lang	=	'heb'	self.binary	=	"tesseract"	self.msgs	=	{	'TS_MISSING':	"""	Unable	to	execute	%s	Make	sure	Tesseract	is	installed	correctly	"""	%	self.	binary,	'TS_VERSION'	:	'Tesseract	version	is	too	old',
  'TS_img_MISSING'	:	'The	specified	tiff	file	could	not	be	found',	'TS_FAILED'	:	'Tesseract-OCR	failed!',	}=	new_init	wow	=	pypdfocr_gs.PyGs(kk)	tt	=	pypdfocr_tesseract.PyTesseract(kk)	def	secFails(file_name,	old_file_name):	wow.make_img_from_pdf(file_name)	files	=	glob.glob("X:/e26cba163	/3063163	/	"	+	'	*.jpg')	for	file	in	files:	im	=
  Image.open(file)	im.save(file	+	".tiff")	files	=	glob.glob("PATH"	+	'*.tiff')	for	file	in	files:	tt.make_hocr_from_pnm(file)	pdftxt	=	""	files	=	glob.glob("PATH"	+	'*.html')	for	file	in	files:	open(file)	as	myFile:	pdftxt	=	pdftxt	+	"#"	+	"	.join	(line.rstrip()	for	line	in	my	file)	findNum(pdftxt,oldfilename)	folder	="PATH"	for	file	os.listdir(folder):	filepath	=
  os.path.join(folder,	file_file)	try	:	if	os	.path	.isfile(file_path):	os.unlink(file_path)	except	e:	print	e	def	pdf2ocr(filename):	pdffile	=	filename	os.system('pypdfocr	-l	heb	'	+	pdffile)	def	ocr2txt(filename	)	:	pdffile	=	filename	output1	=	pdffile.replace(".pdf","_ocr.txt")	output1	=	"PATH"	+	os.p	ath.basename(output1)	input1	=	pdffile.replace(.pdf	","_ocr	.pdf
  ")	os.system("pdf2txt	"	-o	+	output1	+	"	"	+	input1)	with	open(output1)	as	my	file:	pdftxt=	"".join(line.	rstrip()	for	a	line	in	my	file)	findNum(pdftxt,	filename)	def	findNum(pdftxt,pdffile):	l	=	re.findall(r'\b\d+\b',	pdftxt)	output	=	open('PATH'	+	os	.	path.basename(pdffile)	+	'.txt',	'w')	for	i	in	l:	output	.write(",")	output.write(i)	output.close()	def	is_ascii(s):
  return	all	(	ord	(c)	<	128	for	c	in	s)	i	=	0	files	=	glob.glob(path	+	'	\\*.pdf'	)	print	path	print	files	for	file	in	files:	if	file.endswith(.pdf"):	if	is_ascii	(file	):	print	file	pdf2ocr(file)	ocr2txt	(file)	else:	newname	=	"PATH"	+	str(i)	+	".pdf"	Shutil.copyfile(file,	newname)	print	newname	secFile(newname,	file)	i	=	i	+	1	files	=	glob.glob(path	+	'	\\'	+	'*_ocr.pdf')	for
  file	in	files:	file	print	Shutil.copyfile(file,	"PATH"	+	os.path.basename(file))	os	.	remove(file)	os.remove(file)