Scan Handwritten Text into Digital Format with OCR

In today’s digital era, many of us have wondered: how can we copy text from a book into an editable digital format on a laptop or smartphone?

The answer lies in Optical Character Recognition (OCR). This technology simplifies document digitization, making information more accessible and manageable. This article will provide a comprehensive overview of what OCR is, how the technology has developed, and how it works for everyday needs.

What is OCR?

Optical Character Recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents, enabling the text to be edited, searched, and saved in digital format.

With OCR, physical documents like books, magazines, ID cards, or forms can be transformed into accessible and manageable digital data.

OCR has become crucial as digital technologies advance, while at the same time, society still heavily relies on physical documents. Simply scanning documents into digital images isn’t effective because they can't be edited. But with OCR, scanned text can be edited and widely distributed.

This technology is highly beneficial across sectors such as banking, education, and healthcare, where document digitization improves operational efficiency and information accessibility.
Some key benefits of OCR include:

• Reducing document processing costs

• Simplifying information gathering from physical documents

• Accelerating and automating information validation

• Protecting information from damage to physical documents

For individuals who are visually impaired, OCR devices can read text from scanned documents aloud. OCR can also aid language translation by converting text from physical documents into editable digital format and then translating it.

How Does OCR Work?

OCR technology follows a series of processes to transform text images into editable digital text. Here are the general steps, adapted from AWS:

1. Image Acquisition

The first step in OCR is scanning the document. A scanner reads the document and converts it into binary data. OCR software then analyzes the scanned image by distinguishing the lighter areas as the background and the darker areas as the text.

2. Preprocessing

Before recognizing text, the scanned image needs to be cleaned and enhanced for better accuracy. Techniques include:

• Deskewing: Straightening slightly tilted scanned documents so the text lines up properly.

• Despeckling: Removing spots or digital noise from the image and smoothing the edges of the text.

• Cleaning Unnecessary Elements: Removing boxes, lines, or other artifacts in the scanned image.

• Script Recognition: Identifying characters from documents in multiple languages.

3. Text Recognition

The OCR software recognizes text using two main methods:

• Pattern Matching
  This method compares the characters from the image with stored templates (glyphs). If the document's font and size match the database, the text can be recognized accurately. This method works best for documents typed in standard fonts.

• Feature Extraction
  This method breaks down the character shapes into basic features like lines, curves, line directions, and intersections. It then matches these features to stored glyphs, enabling the recognition of various fonts and sizes.

4. Post-Processing

Once the text is recognized, it’s converted into a digital file, like a PDF. This makes printed documents easier to use, edit, and search electronically.

Examples of Optical Character Recognition (OCR) Applications

OCR technology is widely applied across many fields, such as:

• Simplifying digital form filling using data from ID cards (e.g., KTP).

• Converting information from physical documents into digital format.

• Automating data entry and extraction processes.

• Safeguarding important documents in digital storage.

• Extracting text from screenshots.

• Making scanned documents searchable.

• Identity verification in Know Your Customer (KYC) processes.

• Helping individuals with disabilities read printed text.

• Translating text from foreign languages in physical documents.

• Assisting educators in compiling knowledge from printed documents.

Optical Character Recognition (OCR) has become an essential solution for document digitization and processing, enabling various industries to work more efficiently, securely, and accurately.

From simplifying identity verification processes, automating data entry, to improving accessibility, OCR continues to evolve to meet the growing demands of digital transformation.

VIDA offers OCR and document verification solutions as part of its digital identity verification process. Using this technology, VIDA can automatically read data from ID cards, driver’s licenses, passports, and other important documents.