Scan Program: Digitize Document Management Properly

Digitizing document management is simple in theory: documents that already come from electronic sources are not printed out as they used to be, but stored digitally - as PDFs, for example. And: already existing mountains of paper are turned into digital files with a scan program.

However, the real challenge comes afterwards: Companies then have all their data in a digital format. However, this is usually difficult or impossible to search, as this data is digital but cannot be retrieved in a structured way. Information is therefore barely accessible. What's more, companies need to find a way to sort, analyze and evaluate the flood of digital data. This is where OCR technology (Optical Character Recognition) comes into play.

We'll show which OCR scanning software companies can use to extract and organize data from any file format to make their document management efficient.

Benefits of digital Document Management

If companies have digitized their document management with a scan software, they benefit from these advantages:

Uncomplicated access

By digitizing documents, they can be stored, organized and retrieved quickly and easily - regardless of location. This saves time and reduces the effort required for manual searching and sorting. This facilitates collaboration and the exchange of information.

Space saving

Digital, scanned documents do not take up physical space. Unlike paper documents, which take up a lot of space on shelves and in closets, digital documents can be stored on servers or cloud storage platforms.

Security and data protection

Digital documents can be protected by encryption and access rights. This makes it possible to protect sensitive information from unauthorized access and ensure compliance with data protection regulations.

Versioning and revision security

A digital document management system enables versions to be managed and changes to be tracked. This makes it possible to trace the history of a document and ensure revision security.

Workflow automation

Digitally structured document management systems often offer functions for automating workflows. This can speed up editing and approval processes and increase efficiency.

Environmental friendliness

By reducing paper consumption, digital document management systems help protect the environment. Less paper means less resource consumption, less CO² pollution and less waste.

Document Management with an OCR Scan Program

OCR (Optical Character Recognition) is a technology that enables computers to recognize printed or handwritten text and convert it into editable digital formats. What does this look like in practice?

At OCR images or scans of text documents are first created. These images are then analyzed by OCR software to identify the characters they contain. This process takes place in several steps.

OCR Scan Program Use Cases

In practice, an OCR scan program helps in these cases, for example:

Automatic data acquisition for tax audit

Companies can use OCR software to scan tax documents such as receipts, invoices and bank statements and automatically extract the relevant data. This allows tax audits to be performed more efficiently, requirements of Generally Accepted Accounting Principles (GAAP) fulfilled and errors minimized.

Efficient invoice processing

OCR software enables automatic capture and invoice data processing, such as invoice number, supplier data and amounts. This information can then be imported into an electronic invoicing system or accounting software, reducing manual effort and the risk of errors.

Automated processing of application documents

When hiring new employees, companies often have to sift through and process numerous, multi-page documents such as CVs, certificates and application letters. You can use OCR software to scan documents and extract the information they contain. This speeds up and simplifies the applicant selection process.

Scan Programs for efficient Document Management

In general, there are two types of digital documents: Documents created using software such as Microsoft Word, Google Docs or Adobe Acrobat (documents from digital sources), and documents that exist as a scan of a paper document (documents from non-digital sources). What scanning programs can organizations use to extract data from these documents? 

Documents from non-digital sources

Documents that were not created electronically, but consist of a scan of a piece of paper, are usually in the form of an image. Unlike PDFs, for example, images are not easily searchable. A conventional scan program can therefore not read them. It can therefore also not edit, change or adapt the documents. For this, companies need OCR software. It can extract, analyze and evaluate unstructured data from all types of documents. This can be done with these applications, for example:

Pytesseract

Companies can use the OCR engine Tesseract integrated in the Python programming language. Python serves as the backend tool for the OCR algorithms. The OCR capabilities of Python are extended by the "pytesseract" library. It provides an interface to run Tesseract OCR from code written in Python.

Tess4J

Tess4J is a powerful Java library that provides users with wrapper methods for using the Tesseract OCR engine. Developers can therefore easily integrate OCR functions into their Java projects.

Tesseract.NET

Tesseract.NET allows developers to seamlessly integrate Tesseract into C# applications. It provides a well-documented C# wrapper for Tesseract's OCR engine. In practice, this means that companies can use Tesseract.NET to easily extract text from images that have been automatically digitized with a scanner.

How exactly companies can use Pytesseract, Tess4J and Tesseract.Net is shown in our comprehensive practical guide to Tesseract.

Konfuzio

Companies that want to achieve more accurate results with OCR can rely on software from Konfuzio This is particularly powerful for handwriting, special fonts and languages other than English. In order to deliver precise results, Konfuzio uses artificial intelligence.

Machine learning trains OCR systems to better identify and recognize patterns based on large data sets.

In practice, the software can therefore reliably identify even low-resolution images, handwritten text or illegible characters.

Documents from digital sources

To extract data from documents that originate from digital sources, companies can also use one of the OCR applications mentioned above. However, since documents such as PDF files are easier to search, companies can alternatively use these frameworks and libraries of programming languages:

PyPDF2

PyPDF2 is a widely used Python library. Companies can use it to extract text from electronically generated PDF files. In addition, they can also use it to rotate pages, merge multiple pages or split a PDF file, for example. Here is an example of a simple code snippet that can act as PDF scanner software using PyPDF2:

import PyPDF2
def pdf_scanner(file_path):
    # Open the PDF file in binary mode
    with open(file_path, 'rb') as file:
        # Create a PDFReader object
        pdf_reader = PyPDF2.PdfFileReader(file)
        # Iterate over each page of the PDF file
        for page_num in range(pdf_reader.numPages):
            # Read the text on the current page
            page = pdf_reader.getPage(page_num)
            text = page.extractText()
            # Process the extracted text (you can add your own logic here)
            print(f "Page {page_num + 1}:")
            print(text)
            print()
# Example call of the function with a PDF file named "example.pdf
pdf_scanner('example.pdf')

pdfrw

pdfrw is a Python library that companies can use to edit PDF documents. In addition to the ability to capture PDF documents, pdfrw has other functions such as merging scanned files, rotating pages or changing metadata. Here is a simple code example:

import pdfrw
from PIL import Image
def scan_pdf(pdf_path, output_path):
    pdf = pdfrw.PdfReader(pdf_path)
    output_pdf = pdfrw.PdfWriter()
    for page in pdf.pages:
        page_content = page.Contents
        xref = page_content[0].objid
        image = Image.open("scanned_image.jpg")
        image_xref = pdfrw.PdfDict(Type="/XObject", Subtype="/Image", BitsPerComponent=8, Width=image.width, Height=image.height, ColorSpace="/DeviceRGB", Filter="/DCTDecode")
        image_xref.stream = image.tobytes()
        resources = pdfrw.PdfDict(XObject=pdfrw.PdfDict())
        resources.XObject.X1 = image_xref
        page.Contents = pdfrw.PdfArray([pdfrw.PdfIndirect(xref), pdfrw.PdfIndirect(image_xref)])
        page.Resources = resources
        output_pdf.addpage(page)
    output_pdf.write(output_path)
# Example call
scan_pdf("input.pdf", "output.pdf")

Read PDF files in Java

The Java programming language has built-in classes that companies can use to read and write PDF documents. For example, if they use the "PDFTextStripper" class to extract data from a document, it looks like this in the code:

import java.io.File;
import java.io.IOException;
import org.apache.pdfbox.pdmodel.PDDocument;
import org.apache.pdfbox.text.PDFTextStripper;
public class PDFScanner {
    public static void main(String[] args) {
        File file = new File("path_to_pdf_file.pdf");
        try {
            PDDocument document = PDDocument.load(file);
            PDFTextStripper textStripper = new PDFTextStripper();
            String text = textStripper.getText(document);
            System.out.println(text);
            document.close();
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

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