Add OCR support for image based pdfs with lines
* Cosmits * Remove unnecessary kwargs * Direct ghostscript call output to /dev/null * Change char_margin's default value * Add image attribute in Table and Cell * Add OCR * Fix coordinates * Add table_area * Add ocr options to cli * Direct ghostscript call output to /dev/null * Add ocr dostring * Add requirements * Update READMEpull/2/head
Vinayak Mehta
GitHub
parent
70f626373b
commit
970256e19d
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@ -57,6 +57,10 @@ Currently, camelot works under Python 2.7.
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The required dependencies include [numpy](http://www.numpy.org/), [OpenCV](http://opencv.org/) and [ImageMagick](http://www.imagemagick.org/script/index.php).
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### Optional
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You'll need to install [Tesseract](https://github.com/tesseract-ocr/tesseract) if you want to extract tables from image based pdfs. Also, you'll need a tesseract language pack if your pdf isn't in english.
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## Installation
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Make sure you have the most updated versions for `pip` and `setuptools`. You can update them by
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@ -1,3 +1,3 @@
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__version__ = '1.0.0'
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__all__ = ['pdf', 'lattice', 'stream']
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__all__ = ['pdf', 'lattice', 'stream', 'ocr']
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@ -79,6 +79,7 @@ class Cell:
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self.text = ''
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self.spanning_h = False
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self.spanning_v = False
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self.image = None
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def add_text(self, text):
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"""Adds text to cell.
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@ -0,0 +1,148 @@
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import os
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import subprocess
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import pyocr
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from PIL import Image
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from .table import Table
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from .imgproc import (adaptive_threshold, find_lines, find_table_contours,
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find_table_joints)
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from .utils import merge_close_values, encode_list
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class OCR:
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"""Uses optical character recognition to get text out of image based pdfs.
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Currently works only on pdfs with lines.
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Parameters
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----------
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table_area : list
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List of strings of the form x1,y1,x2,y2 where
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(x1, y1) -> left-top and (x2, y2) -> right-bottom in PDFMiner's
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coordinate space, denoting table areas to analyze.
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(optional, default: None)
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mtol : list
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List of ints specifying m-tolerance parameters.
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(optional, default: [2])
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dpi : int
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Dots per inch.
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(optional, default: 300)
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lang : string
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Language to be used for OCR.
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(optional, default: 'eng')
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scale : int
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Used to divide the height/width of a pdf to get a structuring
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element for image processing.
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(optional, default: 15)
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debug : string
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{'contour', 'line', 'joint', 'table'}
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Set to one of the above values to generate a matplotlib plot
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of detected contours, lines, joints and the table generated.
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(optional, default: None)
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"""
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def __init__(self, table_area=None, mtol=[2], dpi=300, lang="eng", scale=15,
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debug=None):
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self.method = 'ocr'
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self.table_area = table_area
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self.mtol = mtol
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self.tool = pyocr.get_available_tools()[0] # fix this
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self.dpi = dpi
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self.lang = lang
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self.scale = scale
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self.debug = debug
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def get_tables(self, pdfname):
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if self.tool is None:
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return None
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bname, __ = os.path.splitext(pdfname)
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imagename = ''.join([bname, '.png'])
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gs_call = [
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"-q", "-sDEVICE=png16m", "-o", imagename, "-r{0}".format(self.dpi),
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pdfname
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]
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if "ghostscript" in subprocess.check_output(["gs", "-version"]).lower():
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gs_call.insert(0, "gs")
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else:
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gs_call.insert(0, "gsc")
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subprocess.call(gs_call, stdout=open(os.devnull, 'w'),
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stderr=subprocess.STDOUT)
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img, threshold = adaptive_threshold(imagename)
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vmask, v_segments = find_lines(threshold, direction='vertical',
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scale=self.scale)
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hmask, h_segments = find_lines(threshold, direction='horizontal',
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scale=self.scale)
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if self.table_area is not None:
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areas = []
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for area in self.table_area:
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x1, y1, x2, y2 = area.split(",")
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x1 = int(x1)
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y1 = int(y1)
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x2 = int(x2)
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y2 = int(y2)
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areas.append((x1, y1, abs(x2 - x1), abs(y2 - y1)))
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table_bbox = find_table_joints(areas, vmask, hmask)
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else:
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contours = find_table_contours(vmask, hmask)
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table_bbox = find_table_joints(contours, vmask, hmask)
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if self.debug:
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self.debug_images = (img, table_bbox)
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self.debug_segments = (v_segments, h_segments)
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self.debug_tables = []
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if len(self.mtol) == 1 and self.mtol[0] == 2:
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self.mtol = self.mtol * len(table_bbox)
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page = {}
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tables = {}
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table_no = 0
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for k in sorted(table_bbox.keys(), key=lambda x: x[1]):
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table_data = {}
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cols, rows = zip(*table_bbox[k])
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cols, rows = list(cols), list(rows)
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cols.extend([k[0], k[2]])
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rows.extend([k[1], k[3]])
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cols = merge_close_values(sorted(cols), mtol=self.mtol[table_no])
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rows = merge_close_values(sorted(rows, reverse=True), mtol=self.mtol[table_no])
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cols = [(cols[i], cols[i + 1])
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for i in range(0, len(cols) - 1)]
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rows = [(rows[i], rows[i + 1])
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for i in range(0, len(rows) - 1)]
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table = Table(cols, rows)
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if self.debug:
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self.debug_tables.append(table)
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table.image = img[k[3]:k[1],k[0]:k[2]]
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for i in range(len(table.cells)):
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for j in range(len(table.cells[i])):
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x1 = int(table.cells[i][j].x1)
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y1 = int(table.cells[i][j].y1)
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x2 = int(table.cells[i][j].x2)
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y2 = int(table.cells[i][j].y2)
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table.cells[i][j].image = img[y1:y2,x1:x2]
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text = self.tool.image_to_string(
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Image.fromarray(table.cells[i][j].image),
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lang=self.lang,
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builder=pyocr.builders.TextBuilder()
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)
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table.cells[i][j].add_text(text)
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ar = table.get_list()
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ar.reverse()
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ar = encode_list(ar)
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table_data['data'] = ar
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tables['table-{0}'.format(table_no + 1)] = table_data
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table_no += 1
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page[os.path.basename(bname)] = tables
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if self.debug:
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return None
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return page
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@ -126,7 +126,7 @@ class Pdf:
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if self.extractor.method == 'stream':
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self.debug = self.extractor.debug
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self.debug_text = []
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elif self.extractor.method == 'lattice':
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elif self.extractor.method in ['lattice', 'ocr']:
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self.debug = self.extractor.debug
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self.debug_images = []
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self.debug_segments = []
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@ -138,7 +138,7 @@ class Pdf:
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if self.extractor.debug:
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if self.extractor.method == 'stream':
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self.debug_text.append(self.extractor.debug_text)
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elif self.extractor.method == 'lattice':
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elif self.extractor.method in ['lattice', 'ocr']:
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self.debug_images.append(self.extractor.debug_images)
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self.debug_segments.append(self.extractor.debug_segments)
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self.debug_tables.append(self.extractor.debug_tables)
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@ -34,6 +34,7 @@ class Table:
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self.cells = [[Cell(c[0], r[1], c[1], r[0])
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for c in cols] for r in rows]
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self.nocont_ = 0
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self.image = None
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def set_all_edges(self):
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"""Sets all table edges to True.
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@ -3,5 +3,7 @@ matplotlib
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nose
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pdfminer
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pyexcel-xlsx
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Pillow
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pyocr
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PyPDF2
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Sphinx
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@ -17,6 +17,7 @@ from PyPDF2 import PdfFileReader
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from camelot.pdf import Pdf
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from camelot.lattice import Lattice
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from camelot.stream import Stream
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from camelot.ocr import OCR
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doc = """
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@ -52,6 +53,7 @@ options:
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camelot methods:
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lattice Looks for lines between data.
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stream Looks for spaces between data.
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ocr Looks for lines in image based pdfs.
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See 'camelot <method> -h' for more information on a specific method.
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"""
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@ -101,6 +103,26 @@ options:
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"""
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ocr_doc = """
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OCR method looks for lines in image based pdfs.
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usage:
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camelot ocr [-t <tarea>] [-m <mtol>] [options] [--] <file>
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options:
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-t, --tarea <tarea> Specific table areas to analyze.
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-m, --mtol <mtol> Tolerance to account for when merging lines
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which are very close. [default: 2]
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-D, --dpi <dpi> Dots per inch, specify image quality to be used for OCR.
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[default: 300]
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-l, --lang <lang> Specify language to be used for OCR. [default: eng]
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-s, --scale <scale> Scaling factor. Large scaling factor leads to
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smaller lines being detected. [default: 15]
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-d, --debug <debug> Debug by visualizing pdf geometry.
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(contour,line,joint,table) Example: -d table
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"""
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def plot_table_barchart(r, c, p, pno, tno):
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row_idx = [i + 1 for i, row in enumerate(r)]
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col_idx = [i + 1 for i, col in enumerate(c)]
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@ -315,6 +337,8 @@ if __name__ == '__main__':
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args.update(docopt(lattice_doc, argv=argv))
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elif args['<method>'] == 'stream':
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args.update(docopt(stream_doc, argv=argv))
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elif args['<method>'] == 'ocr':
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args.update(docopt(ocr_doc, argv=argv))
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vprint = print if args['--verbose'] else lambda *a, **k: None
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filename = args['<file>']
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@ -487,6 +511,69 @@ if __name__ == '__main__':
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except Exception as e:
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logging.exception(e.message, exc_info=True)
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sys.exit()
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elif args['<method>'] == 'ocr':
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try:
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tarea = args['--tarea'] if args['--tarea'] else None
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mtol = [int(m) for m in args['--mtol']]
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manager = Pdf(OCR(table_area=tarea, mtol=mtol, dpi=int(args['--dpi']),
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lang=args['--lang'], scale=int(args['--scale']),
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debug=args['--debug']),
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filename,
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pagenos=p,
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parallel=args['--parallel'],
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clean=True)
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data = manager.extract()
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processing_time = time.time() - start_time
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vprint("Finished processing in", processing_time, "seconds")
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logging.info("Finished processing in " + str(processing_time) + " seconds")
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if args['--plot']:
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if args['--output']:
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pngname = os.path.join(args['--output'], os.path.basename(pngname))
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plot_type = args['--plot'].split(',')
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if 'page' in plot_type:
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for page_number in sorted(data.keys(), key=lambda x: int(x[5:])):
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page = data[page_number]
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for table_number in sorted(page.keys(), key=lambda x: int(x[6:])):
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table = page[table_number]
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plot_table_barchart(table['r_nempty_cells'],
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table['c_nempty_cells'],
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table['empty_p'],
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page_number,
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table_number)
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if 'all' in plot_type:
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plot_all_barchart(data, pngname)
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if 'rc' in plot_type:
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plot_rc_piechart(data, pngname)
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if args['--print-stats']:
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print_stats(data, processing_time)
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if args['--save-stats']:
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if args['--output']:
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scorename = os.path.join(args['--output'], os.path.basename(scorename))
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with open(scorename, 'w') as score_file:
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score_file.write('table,nrows,ncols,empty_p,line_p,text_p,score\n')
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for page_number in sorted(data.keys(), key=lambda x: int(x[5:])):
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page = data[page_number]
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for table_number in sorted(page.keys(), key=lambda x: int(x[6:])):
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table = page[table_number]
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score_file.write('{0},{1},{2},{3},{4},{5},{6}\n'.format(
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''.join([page_number, '_', table_number]),
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table['nrows'],
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table['ncols'],
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table['empty_p'],
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table['line_p'],
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table['text_p'],
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table['score']))
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if args['--debug']:
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manager.debug_plot()
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except Exception as e:
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logging.exception(e.message, exc_info=True)
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sys.exit()
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if args['--debug']:
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print("See 'camelot <method> -h' for various parameters you can tweak.")
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