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Python use OCR to make searchable PDFs and extract text

Sample Python code shows how to use the PDFTron OCR module on scanned documents in multiple languages. The OCR module can make searchable PDFs and extract scanned text for further indexing.

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#---------------------------------------------------------------------------------------
# Copyright (c) 2001-2019 by PDFTron Systems Inc. All Rights Reserved.
# Consult LICENSE.txt regarding license information.
#---------------------------------------------------------------------------------------

import site
site.addsitedir("../../../PDFNetC/Lib")
import sys
from PDFNetPython import *

# Relative path to the folder containing test files.
input_path = "../../TestFiles/OCR/"
output_path = "../../TestFiles/Output/"

# ---------------------------------------------------------------------------------------
# The following sample illustrates how to use OCR module
# --------------------------------------------------------------------------------------

def main():

    # The first step in every application using PDFNet is to initialize the
    # library and set the path to common PDF resources. The library is usually
    # initialized only once, but calling Initialize() multiple times is also fine.
    PDFNet.Initialize()

    # The location of the OCR Module
    PDFNet.AddResourceSearchPath("../../../PDFNetC/Lib/");

    if not OCRModule.IsModuleAvailable():

        print("""
        Unable to run OCRTest: PDFTron SDK OCR module not available.
        ---------------------------------------------------------------
        The OCR module is an optional add-on, available for download
        at http://www.pdftron.com/. If you have already downloaded this
        module, ensure that the SDK is able to find the required files
        using the PDFNet::AddResourceSearchPath() function.""")

    else:

        # Example 1) Process image without specifying options, default language - English - is used
        # --------------------------------------------------------------------------------

        # A) Setup empty destination doc

        doc = PDFDoc()

        # B) Run OCR on the .png with options

        OCRModule.ImageToPDF(doc, input_path + "psychomachia_excerpt.png", None)

        # C) Check the result

        doc.Save(output_path + "psychomachia_excerpt.pdf", 0)
        print("Example 1: psychomachia_excerpt.png")

        # Example 2) Process document using multiple languages
        # --------------------------------------------------------------------------------

        # A) Setup empty destination doc

        doc = PDFDoc()

        # B) Setup options with multiple target languages, English will always be considered as secondary language

        opts = OCROptions()
        opts.AddLang("rus")
        opts.AddLang("deu")

        # C) Run OCR on the .jpg with options

        OCRModule.ImageToPDF(doc, input_path + "multi_lang.jpg", opts)

        # C) Check the result

        doc.Save(output_path + "multi_lang.pdf", 0)
        print("Example 2: multi_lang.jpg")

        # Example 3) Process a .pdf specifying a language - German - and ignore zone comprising a sidebar image
        # --------------------------------------------------------------------------------

        # A) Open the .pdf document

        doc = PDFDoc(input_path + "german_kids_song.pdf")

        # B) Setup options with a single language and an ignore zone

        opts = OCROptions()
        opts.AddLang("deu")

        ignore_zones = RectCollection()
        ignore_zones.AddRect(Rect(1768, 680, 2056, 3044))
        opts.AddIgnoreZonesForPage(ignore_zones, 1)

        # C) Run OCR on the .pdf with options

        OCRModule.ProcessPDF(doc, opts)

        # D) check the result

        doc.Save(output_path + "german_kids_song.pdf", 0)
        print("Example 3: german_kids_song.pdf")

        # Example 4) Process multi-page tiff with text/ignore zones specified for each page,
        # optionally provide English as the target language
        # --------------------------------------------------------------------------------

        # A) Setup empty destination doc

        doc = PDFDoc()

        # B) Setup options with a single language plus text/ignore zones

        opts = OCROptions()
        opts.AddLang("eng")

        ignore_zones = RectCollection()

        # ignore signature box in the first 2 pages
        ignore_zones.AddRect(Rect(1492, 56, 2236, 432))
        opts.AddIgnoreZonesForPage(ignore_zones, 1)

        ignore_zones.Clear()
        ignore_zones.AddRect(Rect(1492, 56, 2236, 432))
        opts.AddIgnoreZonesForPage(ignore_zones, 2)

        # can use a combination of ignore and text boxes to focus on the page area of interest,
        # as ignore boxes are applied first, we remove the arrows before selecting part of the diagram
        ignore_zones.Clear()
        ignore_zones.AddRect(Rect(992, 1276, 1368, 1372))
        opts.AddIgnoreZonesForPage(ignore_zones, 3)

        text_zones = RectCollection()
        # we only have text zones selected in page 3

        # select horizontal BUFFER ZONE sign
        text_zones.AddRect(Rect(900, 2384, 1236, 2480))

        # select right vertical BUFFER ZONE sign
        text_zones.AddRect(Rect(1960, 1976, 2016, 2296))
        # select Lot No.
        text_zones.AddRect(Rect(696, 1028, 1196, 1128))

        # select part of the plan inside the BUFFER ZONE
        text_zones.AddRect(Rect(428, 1484, 1784, 2344))
        text_zones.AddRect(Rect(948, 1288, 1672, 1476))
        opts.AddTextZonesForPage(text_zones, 3)

        # C) Run OCR on the .pdf with options

        OCRModule.ImageToPDF(doc, input_path + "bc_environment_protection.tif", opts)

        # D) check the result

        doc.Save(output_path + "bc_environment_protection.pdf", 0)
        print("Example 4: bc_environment_protection.tif")

        # Example 5) Alternative workflow for extracting OCR result JSON, postprocessing
        # (e.g., removing words not in the dictionary or filtering special
        # out special characters), and finally applying modified OCR JSON to the source PDF document
        # --------------------------------------------------------------------------------

        # A) Open the .pdf document

        doc = PDFDoc(input_path + "zero_value_test_no_text.pdf")

        # B) Run OCR on the .pdf with default English language

        json = OCRModule.GetOCRJsonFromPDF(doc, None)

        # C) Post-processing step (whatever it might be)

        print("Have OCR result JSON, re-applying to PDF")

        OCRModule.ApplyOCRJsonToPDF(doc, json)

        # D) Check the result

        doc.Save(output_path + "zero_value_test_no_text.pdf", 0)
        print("Example 5: extracting and applying OCR JSON from zero_value_test_no_text.pdf")

        # Example 6) The postprocessing workflow has also an option of extracting OCR results in XML format,
        # similar to the one used by TextExtractor
        # --------------------------------------------------------------------------------

        # A) Setup empty destination doc

        doc = PDFDoc()

        # B) Run OCR on the .tif with default English language, extracting OCR results in XML format. Note that
        # in the process we convert the source image into PDF.
        # We reuse this PDF document later to add hidden text layer to it.

        xml = OCRModule.GetOCRXmlFromImage(doc, input_path + "physics.tif", None)

        # C) Post-processing step (whatever it might be)

        print("Have OCR result XML, re-applying to PDF")

        OCRModule.ApplyOCRXmlToPDF(doc, xml)

        # D) Check the result

        doc.Save(output_path + "physics.pdf", 0)
        print("Example 6: extracting and applying OCR XML from physics.tif")


if __name__ == '__main__':
    main()
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