How to calculate and improve OCR accuracy

How to calculate and improve OCR accuracy

Imagine having to copy down articles from legacy documents or scanned images. Manually typing text is feasible only if the volume of data is relatively low. It doesn’t make much sense for businesses to employ manual labor for this task. Companies deploy OCR solutions to automate the process of scanning information and converting it into an editable format.

An Optical character recognition or OCR program can extract textual information from scanned documents, images, or pdf. The OCR software can read out single letters on the image, identify them, put them into words, and form sentences.

OCR software uses advanced AI methods such as ICR (Intelligent character recognition) to identify complexities in language or various styles of handwriting. Further advancements in the technology aim towards improving OCR accuracy.

The need to design optical character recognition was recognized to help people with visual impairment. While there is ambiguity over when the first image to text devices was conceived. History dates the development of OCR systems back to the 1800s, but the first patent for an OCR device was done in Germany in 1929 and then in the United States in 1953 by Gustav Tauschek.

Kurzweil Computer Products, Inc., started by Ray Kurzweil in 1974, came out with an Omni-font OCR product that could recognize printed text in any font. While it was developed as a reading machine for the blind, which could read out text aloud, it was later sold to Xerox in 1980, who were interested in commercializing the text-conversion technology.

OCR devices gained immense popularity in the 1990s when it was being used to digitalize historical newspapers. The present systems can deliver near-perfect OCR accuracy and have the ability to automate complex document-processing workflows.

How Does OCR Work?

OCR systems implements hardware and software technology to convert scanned documents or images into textual information. It uses an optical scanner or a specialized circuit board that handles the reading of the text. OCR software is responsible for the advanced processing of understanding letters in the image and forming complete sentences.

The scanner analyzes the light and dark areas of the document. White areas are identified as the background, and the black areas are characters to be recognized. The OCR software then processes the black areas for letters, numeric digits, or symbols.

OCR systems use one of the two algorithms to identify the characters in the scanned document:

  1. Pattern Recognition- the program compares the characters in the document or image with the examples of fed text.
  2. Feature Recognition- It uses the features of alphabets, numeric digits, or symbols such as angled lines, crossed lines, and curves to recognize characters.

How to Calculate OCR accuracy?

The OCR accuracy of the software is calculated by comparing the text output with the original document. The reliability of the transformed text is measured using two metrics:

  • Character Accuracy
  • Word Accuracy

Generally, accuracy on a character level is used to measure the OCR accuracy. The accuracy of the OCR software depends on how many times a character is recognized correctly versus the number of times it is identified incorrectly.

For example, if 1 out of 100 characters is uncertain, OCR software accuracy comes to be 99%.

If 1 out of 1000 characters is recognized incorrectly, OCR accuracy becomes 99.9%.

How to Improve OCR accuracy?

One aims to improve character level accuracy as a better character accuracy will translate to better word and document accuracy. Let us understand how you can improve your OCR accuracy rate.

Enhance OCR Engine

The OCR engine is the software that does all the heavy lifting. Different OCR software use different algorithms and have their unique pros and cons. The OCR software that suits your needs best depends on its use, system integration, and budget.

Improve Quality of Source Image

OCR systems can offer better results if the source image is higher quality. OCR is likely to include errors if the source is not clear or readable. The quality of the image can be optimized for OCR scanning in the following ways:

  • Increasing contrast for clarity
  • Enhance the sharpness of characters
  • Proper aligning
  • Avoid scanning artifacts
  • Reduce pixel noise

Benefits of OCR

Even if the convenience of quick scans is tough to beat, OCR systems offer much more benefits to the user.

Enhanced Productivity

OCR programs improve your business productivity by simplifying text search, editing, and access solutions. Employees can focus on more critical tasks to enhance growth and productivity.

Text Editing

OCR programs not only converts scanned document and images into text but also enable editing of the text document. This comes in handy, especially when edits are required on any legal document or invoice.

Save Up on Storage Space

You can dump your legacy file cabinets that carry essential business information and save up on storage space by making them digital.

Time Management

OCR systems help you save up on a lot of valuable time previously used in manually typing the text information from images or scanned documents. Also, it makes it much quicker to access a specific piece of information.

Text Searches

Searching for the right file and document can be a hassle. Converting hard copies into digital information makes it easier to search records by highlighting texts or phrases.

OCR Accuracy Tool from Klearstack

OCR accuracy tool from Klearstack will help you save your precious time and resources. Businesses can deploy OCR solutions to process textual information easily and quickly.

OCR for your business makes sense because:

  • Eliminates manual data entry
  • Reduces errors
  • Save up on storage space
  • Enhance productivity

Conclusion

OCR systems can extract textual information from digital images or scanned documents which is only one aspect of data capturing. A robust data capturing solution can accelerate business functions and open automation lanes for content processing.

Ashutosh Saitwal
Ashutosh Saitwal
www.klearstack.com/

Ashutosh is the founder and director of the award winning KlearStack AI platform. You can catch him speaking at NASSCOM events around the world where he speaks and is an evangelist for RPA, AI, Machine Learning and Intelligent Document Processing.