What is Optical Character Recognition (OCR)?
Optical Character Recognition (OCR) is a process that converts an image of text into a machine-readable text format. For example, if you back up a form or receipt, your computer stores the copy as an image file. A text editor cannot be used to edit, search, or read the words in the image file. However, OCR can transform the image into a text document, making its content accessible as readable text data.
Why is OCR important?
Most business workflows involve extracting information from printed media. Printed forms, invoices, scanned legal documents, and printed contracts are essential in business processes. Managing and storing these large volumes of documents requires significant time and space. While document management in digital format is recommended, digitization presents challenges. The process requires manual effort and can be tedious and slow.
Additionally, digital transformation of document content often creates image files where the text is embedded. The content of these images cannot be processed in the same way as text documents using word processing software. OCR technology solves this issue by converting text images into readable data, which can then be processed by business applications. This allows companies to analyze information, streamline operations, automate workflows, and boost productivity.
How Does OCR Work?
The OCR software or engine follows these steps:
1. Visual Identity Acquisition
A scanner processes documents and converts them into binary data. The OCR program examines the scanned image, categorizing light areas as the background and dark areas as text.
2. Preprocessing and Image Optimization
Before reading the text, the OCR program cleans the image and removes distortions to enhance accuracy. Some preprocessing techniques include:
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Straightening or deskewing the scanned document to correct alignment issues.
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Removing or smoothing imperfections from the digital image.
- Enhancing contrast and lines in the illustration.
- Identifying hyphens for multilingual OCR processing.
3. Text Recognition
OCR software uses two main algorithms for text recognition: pattern matching and feature extraction.
4. Pattern Matching
Pattern matching extracts an image of a character, known as a glyph, and compares it with a stored glyph of a similar shape. This method works best when the stored glyph uses the same font and scale as the input glyph. It is most effective for digitized images of documents in standard fonts.
5. Feature Extraction
Feature extraction breaks down glyphs into key elements like lines, loops, angles, and intersections. The software then analyzes these characteristics to determine the closest match to a stored glyph.
6. Post-Processing
After text recognition, the system converts the extracted text into a digital file. Some OCR systems can generate searchable PDFs that contain both the original scanned image and its converted text version for easy reference.
Types of OCR
Software simple de reconocimiento óptico de caracteres
A simple OCR engine stores multiple text patterns and font styles as templates. It uses pattern-matching algorithms to compare text images, letter by letter, against an internal database. If the system aligns text word by word, it is called optical word recognition. This method has limitations since there are countless fonts and handwriting styles, making it impossible to capture all variations in a database.
Software inteligente de reconocimiento óptico de caracteres
Modern OCR systems use Intelligent Character Recognition (ICR) technology, enabling them to read text similarly to how humans do. These systems leverage machine learning techniques to enhance accuracy. A neural network-based learning system processes text at multiple levels, repeatedly analyzing the image. It examines different characteristics such as curves, lines, intersections, and loops, integrating all these elements to generate an accurate text output. While ICR typically processes one character at a time, it does so rapidly, delivering results in seconds.
Intelligent Word Recognition (IWR)
IWR follows the same principles as ICR but processes entire words instead of individual characters, improving speed and accuracy in text recognition.
Optical Mark Recognition (OMR)
OMR technology identifies logos, watermarks, and other textual symbols in a document. It is commonly used for detecting checkboxes, barcodes, and special marks in forms and surveys.
