Optical Character Recognition (OCR) systems convert documents into machine-readable text, allowing data to be extracted from images and text files and made searchable. Modern OCR tools also enable practical conversions, such as JPG to Word, making it easier to turn scanned images into editable documents. This article explores the nine types of documents that OCR systems can recognize.

1. Document quality

The quality of a document is a major factor in OCR performance. Text recognition relies on predefined algorithms to identify patterns. Low-quality images with blurry text, noise, or distorted tables can reduce the accuracy of data extraction. Techniques like denoising, deskewing, binarization, and resizing can improve document quality.

2. Type of OCR technology

There are several types of OCR, each with different capabilities:

1. Simple OCR software: Uses pattern-based matching to compare text images character by character against its database. It cannot read handwritten text.
2. Intelligent character recognition (ICR): Uses advanced methods like machine learning to analyze text much like a human does. It processes images one character at a time but delivers results in seconds.
3. Intelligent word recognition: Similar to ICR, but it processes whole words instead of individual characters.
4. Optical mark recognition: Identifies non-text elements like logos and watermarks.

3. OCR training

OCR algorithms can be trained to handle new document types. Powered by machine learning, the software analyzes new layouts and adapts to them, which can decrease error rates and increase accuracy to as high as 99%.

Types of documents processed by OCR systems

Not all OCR systems can handle every document type; some require special training to process custom layouts.

1. Printed text

This is the most common document type for OCR and includes books, articles, invoices, receipts, and other printed materials. OCR can convert these physical documents into editable digital formats, like Word files or searchable PDFs, with nearly 99.9% accuracy. Companies often digitize printed documents to create an easily searchable cloud-based archive.

2. Business documents

Businesses use a wide variety of documents, such as invoices, contracts, financial statements, and bank statements. An OCR platform can scan these documents and store them in a central repository, making retrieval easier for employees, auditors, and managers. The collected data can also be analyzed for business insights.

3. Official forms and applications

Organizations in regulated industries often require customers to fill out numerous forms. For example, banks and lenders process extensive loan applications. Businesses also handle official paperwork like tax and registration forms. OCR simplifies these workflows by extracting the necessary data and uploading it in a structured format, allowing departments to track and archive documents efficiently.

4. Handwritten documents

A key feature of modern OCR is its ability to read handwriting. It works best with block letters and often struggles with cursive, as cursive writing is less consistent. Advanced OCR can identify neat cursive with good accuracy, but there is still a higher probability of errors compared to printed text.

5. ID cards

OCR systems can extract information from passports, driver’s licenses, and other ID cards. The system uses key-value pairs to identify and extract important data fields, such as name, address, and date of birth. This feature helps banks and financial institutions streamline their customer onboarding and KYC processes.

6. Industry-specific documents

Healthcare: OCR has two main uses in healthcare:

1. Medical records: OCR helps hospitals digitize patient records, format them automatically, and store them in a compliant, centralized cloud hub. This helps healthcare organizations comply with HIPAA privacy regulations.
2. Insurance claims: OCR can speed up the claims process, reducing paperwork and allowing hospitals to discharge patients more quickly.

Education and Training: Companies use OCR to digitize guidebooks and training documents, creating a knowledge base for customers and employees. This can improve employee onboarding and customer satisfaction.

7. Multilingual documents

OCR can process documents in multiple languages by recognizing the language and applying the correct model. However, accuracy can decrease when multiple languages appear in the same line or paragraph due to contextual limitations.

8. Documents with tables

OCR can efficiently process tables by identifying fields, columns, and checkboxes. However, complex tables with merged or irregularly sized cells can challenge the system’s ability to recognize the structure and extract data accurately.

9. Documents with images

OCR can extract text from images, including logos and watermarks, which is useful for redacting sensitive information. However, complex images with non-standard or distorted fonts can be difficult for the platform to read accurately. Despite these challenges, OCR is a valuable tool for making image-based content searchable and editable.