import itertools import os import re import subprocess from multiprocessing.pool import Pool import langdetect import pyocr from django.conf import settings from documents.parsers import DocumentParser, ParseError from PIL import Image from pyocr.libtesseract.tesseract_raw import \ TesseractError as OtherTesseractError from pyocr.tesseract import TesseractError from .languages import ISO639 class OCRError(Exception): pass class RasterisedDocumentParser(DocumentParser): """ This parser uses Tesseract to try and get some text out of a rasterised image, whether it's a PDF, or other graphical format (JPEG, TIFF, etc.) """ CONVERT = settings.CONVERT_BINARY DENSITY = settings.CONVERT_DENSITY if settings.CONVERT_DENSITY else 300 THREADS = int(settings.OCR_THREADS) if settings.OCR_THREADS else None UNPAPER = settings.UNPAPER_BINARY DEFAULT_OCR_LANGUAGE = settings.OCR_LANGUAGE def get_thumbnail(self): """ The thumbnail of a PDF is just a 500px wide image of the first page. """ run_convert( self.CONVERT, "-scale", "500x5000", "-alpha", "remove", self.document_path, os.path.join(self.tempdir, "convert-%04d.png") ) return os.path.join(self.tempdir, "convert-0000.png") def get_text(self): images = self._get_greyscale() try: return self._get_ocr(images) except OCRError as e: raise ParseError(e) def _get_greyscale(self): """ Greyscale images are easier for Tesseract to OCR """ # Convert PDF to multiple PNMs pnm = os.path.join(self.tempdir, "convert-%04d.pnm") run_convert( self.CONVERT, "-density", str(self.DENSITY), "-depth", "8", "-type", "grayscale", self.document_path, pnm, ) # Get a list of converted images pnms = [] for f in os.listdir(self.tempdir): if f.endswith(".pnm"): pnms.append(os.path.join(self.tempdir, f)) # Run unpaper in parallel on converted images with Pool(processes=self.THREADS) as pool: pool.map(run_unpaper, itertools.product([self.UNPAPER], pnms)) # Return list of converted images, processed with unpaper pnms = [] for f in os.listdir(self.tempdir): if f.endswith(".unpaper.pnm"): pnms.append(os.path.join(self.tempdir, f)) return sorted(filter(lambda __: os.path.isfile(__), pnms)) def _guess_language(self, text): try: guess = langdetect.detect(text) self.log("debug", "Language detected: {}".format(guess)) return guess except Exception as e: self.log("warning", "Language detection error: {}".format(e)) def _get_ocr(self, imgs): """ Attempts to do the best job possible OCR'ing the document based on simple language detection trial & error. """ if not imgs: raise OCRError("No images found") self.log("info", "OCRing the document") # Since the division gets rounded down by int, this calculation works # for every edge-case, i.e. 1 middle = int(len(imgs) / 2) raw_text = self._ocr([imgs[middle]], self.DEFAULT_OCR_LANGUAGE) guessed_language = self._guess_language(raw_text) if not guessed_language or guessed_language not in ISO639: self.log("warning", "Language detection failed!") if settings.FORGIVING_OCR: self.log( "warning", "As FORGIVING_OCR is enabled, we're going to make the " "best with what we have." ) raw_text = self._assemble_ocr_sections(imgs, middle, raw_text) return raw_text raise OCRError("Language detection failed") if ISO639[guessed_language] == self.DEFAULT_OCR_LANGUAGE: raw_text = self._assemble_ocr_sections(imgs, middle, raw_text) return raw_text try: return self._ocr(imgs, ISO639[guessed_language]) except pyocr.pyocr.tesseract.TesseractError: if settings.FORGIVING_OCR: self.log( "warning", "OCR for {} failed, but we're going to stick with what " "we've got since FORGIVING_OCR is enabled.".format( guessed_language ) ) raw_text = self._assemble_ocr_sections(imgs, middle, raw_text) return raw_text raise OCRError( "The guessed language is not available in this instance of " "Tesseract." ) def _ocr(self, imgs, lang): """ Performs a single OCR attempt. """ if not imgs: return "" self.log("info", "Parsing for {}".format(lang)) with Pool(processes=self.THREADS) as pool: r = pool.map(image_to_string, itertools.product(imgs, [lang])) r = " ".join(r) # Strip out excess white space to allow matching to go smoother return strip_excess_whitespace(r) def _assemble_ocr_sections(self, imgs, middle, text): """ Given a `middle` value and the text that middle page represents, we OCR the remainder of the document and return the whole thing. """ text = self._ocr(imgs[:middle], self.DEFAULT_OCR_LANGUAGE) + text text += self._ocr(imgs[middle + 1:], self.DEFAULT_OCR_LANGUAGE) return text def run_convert(*args): environment = os.environ.copy() if settings.CONVERT_MEMORY_LIMIT: environment["MAGICK_MEMORY_LIMIT"] = settings.CONVERT_MEMORY_LIMIT if settings.CONVERT_TMPDIR: environment["MAGICK_TMPDIR"] = settings.CONVERT_TMPDIR subprocess.Popen(args, env=environment).wait() def run_unpaper(args): unpaper, pnm = args subprocess.Popen( (unpaper, pnm, pnm.replace(".pnm", ".unpaper.pnm"))).wait() def strip_excess_whitespace(text): collapsed_spaces = re.sub(r"([^\S\r\n]+)", " ", text) no_leading_whitespace = re.sub( "([\n\r]+)([^\S\n\r]+)", '\\1', collapsed_spaces) no_trailing_whitespace = re.sub("([^\S\n\r]+)$", '', no_leading_whitespace) return no_trailing_whitespace def image_to_string(args): img, lang = args ocr = pyocr.get_available_tools()[0] with Image.open(os.path.join(RasterisedDocumentParser.SCRATCH, img)) as f: if ocr.can_detect_orientation(): try: orientation = ocr.detect_orientation(f, lang=lang) f = f.rotate(orientation["angle"], expand=1) except (TesseractError, OtherTesseractError): pass return ocr.image_to_string(f, lang=lang)