Make PDF searchable: With OCR in 5 steps

In the context of digitization, companies today mainly have two types of PDF files:

1. PDFs created digitally using software such as Microsoft Word, Adobe Acrobat or Google, and
2. PDFs (or JPGs) that are available as a scan of a paper document.

Companies do have digital documents in this way. However, depending on the type of PDF, these are difficult to search. This also means that companies can only find and process data with a great deal of effort.

This is where PDF text recognition comes into play. This can be done simply and automatically using optical character recognition (OCR) technology. We explain how companies can use software not only to make PDFs searchable, but also to sort, analyze and evaluate the data obtained from the files.

Making PDF searchable: How OCR works

OCR enables organizations to capture printed, handwritten, or digital text in a PDF (and any other digital format) and convert it into editable formats. How does it work exactly?

In simple terms, OCR software analyzes PDF files and recognizes the characters they contain. In practice, this is done in the following steps:

Make PDF searchable: Benefits of OCR

When companies create searchable PDF files, this is how they benefit in practice:

Lower document management costs

When companies make PDF automatically searchable, they can access relevant data quickly and easily. This saves time and thus costs.

Better data analysis

Since the collected data is (almost) error-free and complete, companies can analyze and scrutinize it with high accuracy and better aligned with their business goals. In this way, they have relevant information and can thus make informed decisions. 

Release of resources

If companies can make PDF searchable on Linux, Mac or Windows, employees are less busy searching and analyzing data. They can therefore devote themselves to more important tasks.

Making PDF searchable: 3 common use cases

To better understand the benefits of using OCR software to make PDF searchable, let's take a look at 3 classic use cases:

Efficient document processing

Companies that daily receive Invoices, receipts and vouchers can easily and quickly process and assign the data they contain and pass it on to subsequent workflows.

For example, OCR software can extract invoice numbers, vendor data, or payment amounts and transfer them to an electronic system such as accounting software.

This reduces manual effort and lowers the risk of errors.

Uncomplicated data acquisition for tax audit

So that companies do not have to go to great lengths to collect the tax data of the past year, they can determine it automatically, collect it and pass it on to the tax department in an orderly manner. In this way, the tax department has direct access to all relevant tax documents such as invoices, receipts and bank statements. A tax audit thus runs more efficiently and fulfills the requirements of generally accepted accounting principles.

More efficient employee search

Companies that are constantly looking for new employees receive a large number of applications. These are usually in PDF format. If companies can make PDFs automatically searchable, they can sift through documents such as resumes, references and cover letters more quickly. OCR software can extract the relevant data and prepare it in such a way that companies can make faster employee decisions.

Making PDF searchable: 7 powerful tools

To make PDF searchable, organizations need powerful software. Which software is suitable depends on the type of source PDFs, image-based documents or scans come from:

Documents from non-digital sources

Scanned documents are not as easily searchable. Traditional programs cannot read or process them. To extract and analyze unstructured data from these documents, companies can use these applications, among others:

Pytesseract

Pytesseract is an OCR engine which is written into the programming language Python Python acts as the backend application for the OCR algorithms. The Pytesseract library extends the existing OCR capabilities of Python. The library forms an interface to run Tesseract OCR from code written in Python.

Tesseract.NET

Tesseract.NET makes it possible to integrate Tesseract into C# applications. For this purpose, it has a C# wrapper for Tesseract OCR. In this way, companies can, for example, make scans that are available as PDFs searchable.

Tess4J

Tess4J is a Java library. It provides companies with wrapper methods for using the Tesseract OCR engine. Developers can thus implement the functions of OCR in their Java projects.

Konfuzio

Companies that want to obtain particularly accurate results and prepare, analyze and evaluate the data, can use Konfuzio - a German OCR software.

Unlike the other technologies mentioned, Konfuzio is also particularly powerful with languages other than English, special fonts, handwritten and scanned documents, and low-resolution images.

To do this, Konfuzio uses artificial intelligence. Machine learning trains the OCR systems to recognize patterns even in enormously large data sets.

Documents from digital sources

Documents from digital sources are often in PDF format. To make PDF searchable, companies can also rely on the tools mentioned above. However, since the file format is basically easier to search than a scanned image, the following tools are also suitable for this purpose:

PyPDF2

The Python library PyPDF2 enables companies to extract text from digitally generated PDF files. In doing so, it can also split the files, merge multiple pages and rotate them. In practice, code that works with PyPDF2 as a PDF scanner may look like this:

import PyPDF2
def pdf_scanner(pdf_file_path, keyword):
    try:
        with open(pdf_file_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfFileReader(file)
            num_pages = pdf_reader.getNumPages()
            found_pages = []
            for page_num in range(num_pages):
                page = pdf_reader.getPage(page_num)
                text = page.extractText().lower()
                if keyword.lower() in text:
                    found_pages.append(page_num + 1)
            return found_pages
    except FileNotFoundError:
        print(f"Datei '{pdf_file_path}' wurde nicht gefunden.")
        return []
if __name__ == "__main__":
    pdf_file = "beispiel.pdf"  # Passe den Dateipfad entsprechend an
    suchwort = "Python"       # Passe das Suchwort an
    gefunden = pdf_scanner(pdf_file, suchwort)
    if gefunden:
        print(f"Das Suchwort '{suchwort}' wurde auf folgenden Seiten gefunden: {gefunden}")
    else:
        print(f"Das Suchwort '{suchwort}' wurde nicht im PDF gefunden.")

Read PDF files in Java

Java has built-in classes that are suitable for reading and writing PDF files. For example, companies can use the "PDFTextStripper" class to extract information from a document. As code, this could look like this, for example: 

import java.io.IOException;
import org.apache.pdfbox.pdmodel.PDDocument;
import org.apache.pdfbox.text.PDFTextStripper;
public class PDFTextExtractor {
    public static void main(String[] args) {
        try {
            // Pfad zum PDF-Dokument
            String pdfFilePath = "pfad/zum/deinem/pdf/dokument.pdf";
            // PDDocument-Objekt erstellen
            PDDocument document = PDDocument.load(new File(pdfFilePath));
            // PDFTextStripper-Objekt erstellen
            PDFTextStripper textStripper = new PDFTextStripper();
            // Text aus dem Dokument extrahieren
            String text = textStripper.getText(document);
            // Das extrahierte Textergebnis ausgeben
            System.out.println(text);
            // PDDocument schließen
            document.close();
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

In this example, companies would need to ensure that they have included the Apache PDFBox library as a dependency in their project. You can download the library from the official Apache PDFBox website and integrate into their project.

The example shown above loads the PDF document, extracts all the text from it and outputs it to the console. Companies can further process the result according to their requirements to extract and analyze specific data from the document.

pdfrw

With the Python library pdfrw, companies can make an Adobe PDF file searchable and edit it. In addition, pdfrw can also, for example, merge files, rotate individual pages and change the metadata. Here is a code example from practice:

import pdfrw
def suche_nach_information_in_pdf(pdf_datei, suchbegriff):
    pdf_obj = pdfrw.PdfReader(pdf_datei)
    gefundene_seiten = []
    for seite_nr, seite in enumerate(pdf_obj.pages, start=1):
        seite_text = ""
        for annot in seite.Annots:
            if annot.Subtype == "/Widget" and annot.A and annot.A.V:
                seite_text += annot.A.V
        if suchbegriff in seite_text:
            gefundene_seiten.append(seite_nr)
    return gefundene_seiten
if __name__ == "__main__":
    pdf_datei = "pfad/zum/dein_pdf.pdf"
    suchbegriff = "Dein Suchbegriff"
    gefunden_auf_seiten = suche_nach_information_in_pdf(pdf_datei, suchbegriff)
    if gefunden_auf_seiten:
        print(f"Der Suchbegriff '{suchbegriff}' wurde auf folgenden Seiten gefunden:")
        print(gefunden_auf_seiten)
    else:
        print(f"Der Suchbegriff '{suchbegriff}' wurde im PDF-Dokument nicht gefunden.")

Making PDF searchable: How it works with Konfuzio

To make a PDF searchable with Konfuzio, first create a new project in your account and select the function you want to use for a document in the bar at the top. Let's assume that you want to make a handwritten document searchable. You then upload a photo from it as a JPG, for example. 

Konfuzio now automatically detects all characters and words in the document. You can then export the photo as a PDF. Konfuzio makes sure that the font size is exactly the same as in the original document. You can now search the PDF for individual words or correct the text in Konfuzio's SmartView. How this process looks and works in the Konfuzio interface, we show clearly in this tutorial for OCR for text recognition.

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