August 26, 2022

How to Improve OCR accuracy - Get 99% Accuracy With Our Guide

Over the last several decades, advancements in computing power, machine learning techniques, and the complexity of Optical Character Recognition (OCR) algorithms have led to steadily rising accuracy rates. However, OCR accuracy of 99% or more significant is still relatively rare and not easy to obtain.

There has been a dramatic decline in the consumption of traditionally printed media like books and newspapers, thanks to the widespread adoption of digital alternatives in recent decades. In our species, only seeing the visual form of these written items is sufficient to comprehend their meaning. There is text on the picture; we recognize it and can read it. Computers are different. They need information that is both more clear and more structured for their needs. Optical Character Recognition(OCR) is used for this purpose. 

Scanning bank accounts, invoices, receipts, handwritten papers, coupons, identification of automobile number plates, and much more are just a few of the many uses for optical character recognition.

The Origins of Optical Character Recognition


It was realized that persons with a visual impairment might benefit from developing optical character recognition technology. However, the origin of the earliest image-to-text converters is unclear. It is believed that OCR systems were initially developed in the 1800s, although Gustav Tauschek was the first to patent an OCR device in 1929 in Germany and again in 1953 in the USA. 

In 1974, Ray Kurzweil launched the company Kurzweil Computer Products, Inc., where they released the Omni-font OCR device, which could read printed text in any font. Originally intended as a reading machine for the blind, it was sold to Xerox in 1980 because of the company's interest in commercializing text-conversion technology. 

In the 1990s, OCR technology saw explosive growth as it was employed to digitize old newspapers. In addition to near-perfect OCR accuracy, modern systems may also automate intricate document-processing procedures. 

What is the Process of Optical Character Recognition? 

Optical character recognition (OCR) systems use computer hardware and software to translate scanned text from pictures. For the actual reading of the text, it employs either an optical scanner or a dedicated circuit board. OCR software is what does all the heavy lifting of deciphering text from an image and constructing whole sentences. 

The scanner reads the document's contrast between bright and dark parts. Backgrounds are indicated by white, while characters are shown in black. The OCR program analyzes the black spaces, which may include text in the form of letters, numbers, or symbols. 

OCR systems use both techniques to determine which characters are present on a scanned page. 

Using a comparison of the document's or image's characters to previously fed instances of text, the algorithm can recognize patterns. 

Feature Recognition- It employs the characteristics of alphabets, numeric digits, or symbols such as angled lines, crossing lines, and curves to recognize characters.

Defining OCR Accuracy 

The OCR accuracy may be determined in two ways: 

  • Accuracy on a character level
  • Accuracy on a word level

Evaluation of optical character recognition (OCR) accuracy typically occurs at the character level. How accurate an OCR program is on a character level relies on how frequently a character is adequately identified against how often a character is recognized wrong. If you want to know how confident you can be in each character, you need an accuracy of 99%. While 99.9 percent precision only leaves 0.1 percent of text up to interpretation. 

The accuracy of optical character recognition (OCR) may be evaluated by comparing the results of an OCR run on an image to the source text. After that, you may either count how many words were identified correctly (word level accuracy) or how many characters were detected correctly (character level accuracy) (word level accuracy). 

Most OCR algorithms include linguistic context to increase word-level accuracy. If the text's language is understood (say, English), then the detected words may be cross-referenced with an exhaustive list of all possible terms (e.g., all words in the English language corpus). Once the dictionary entry with the most incredible resemblance is located, the term may be used to "fix" the word that contains the questionable letter.

There are Two Main Variables to Consider While Working to Improve OCR Accuracy 

Original Image's Resolution and Clarity 

Good OCR results can be achieved if the quality of the original source picture is high, that is if human eyes can make out a lot of detail in the original. However, OCR findings are prone to inaccuracy if the original source is unclear. OCR accuracy increases with the clarity of the original source picture since it is simpler to isolate the text from the background. 

Processor for Optical Character Recognition 

The component of an OCR system is responsible for attempting to extract text from an image. A wide selection of OCR engines is available, from open-source alternatives that are free to use to expensive proprietary ones. Even though many OCR engines use the same general class of algorithms, these programs vary in performance depending on several factors. 

The accuracy of optical character recognition (OCR) systems is challenging to compare. The best OCR engine for your needs will vary depending on your use case, available budget, and the complexity of your current infrastructure. 

Example- It seems that Tesseract is now the most used open source OCR engine. Tesseract's optical character recognition (OCR) accuracy is already rather good out of the box, and it may be improved even more with a well-constructed Tesseract picture preprocessing pipeline. 

Benefits of OCR 

Even though the ease of rapid scans is challenging to surpass, OCR technologies provide considerably more advantages to the user. 

Augmented Performance

OCR tools boost your company’s efficiency by simplifying text search, editing, and access options. Employees may concentrate on more vital duties to promote development and productivity. 

Text Alteration 

OCR applications not only turns scanned document and photos into text but also allow modification of the written content. This is helpful, mainly when revisions are necessary on any legal document or invoice. 

Save Up on Storage Capacity 

You may ditch outdated file cabinets that store vital corporate information and save up on storage space by turning them digital. 

Meeting Deadlines 

OCR technologies allow you to save up on a lot of precious time formerly invested in manually inputting the text information from photos or scanned documents. Also, it makes locating a piece of meticulous detail much faster. 

Text Lookup 

Searching for the proper file and document might be a chore. Paper documents may be searched more quickly by highlighting relevant language or phrases in a digital format.

The Significant Uses of OCR in Different Sectors are Discussed Below:


Fraud Mitigation: Use OCR to extract digital information from physical documents (both history and current) and analyze pertinent information to identify any discrepancies between the contract and the claims and the circumstances and situations surrounding the allegations. 

• Risk Adjustments: To accurately determine the premium, identify the risks related to the insurance (health or personal) via the digital extraction of data from claims documents. In addition, identify the conditions associated with specific high-medium-low risk profiles. 


• Digitization of customer records: Digital copies of offline surveys, contract forms, and customer papers provide rich data that, when codified, enhances the customer’s 360-degree profile and enables real-time cross-source assessment of customer information. 


• Digitizing Patient Records: Most patient records are physical records containing detailed information about a patient's past experiences, current medical conditions, and limitations. These records can be digitized and recorded as structured data, which can then be examined for real-time medical evaluation, re-admission analysis, and treatment anomalies. 

• Disease Diagnosis and Patient-at-Risk: OCR plus CAD (Computer-aided Diagnosis) may assist in identifying pre-conditions and serious medical problems from digital data (s). 

Production and Telecommunications. 

• Vendor Contract Assessment: Digital vendor documentation, contract forms, and offline surveys all provide rich information that, when formalized, enhances the vendor's 360-degree profile and aids in the real-time assessment of client data from various sources.

Methods for Better OCR Image Recognition Accuracy 

Assume for the moment that you have chosen an OCR tool. This leaves us with just one variable to tweak to increase OCR's precision: the quality of the original picture.  As was previously said, OCR accuracy improves with increased picture quality.  But what exactly do we mean by "picture quality" here? Simply put, this means "making it as simple as possible" for the OCR engine to pick out a character from its surrounding text. Thus, we aim to have 

  • Sharp character borders
  • High contrasts
  • Well-aligned characters and
  • Less pixel noise as possible

Automated OCR image processing filters available in most engines may vastly enhance the quality of a scanned document's text. Unfortunately, you may be unable to modify these in-built filters to better suit your specific needs. 

In our experience, knowing every preprocessing step and changing the preprocessing settings separately is important to boost OCR performance. 

Here is How to Increase the Accuracy of OCR Results by Preparing your Images: 

Source Material of the Highest Quality 

This is being repeated on purpose, yes. A high-quality picture source is a foundation for reliable OCR conversions. Verify that the paper document hasn't been creased, smudged, or printed with insufficient contrast. Having any of these in the source file will cause the final product to be muddy. So, pick the cleanest and the most source of the file to be transformed. 

For this essay, let’s look at an example photograph of not perfect quality. The example picture below is in dire need of binarization, deskewing, and removal of scanning artifacts. 

Fitting the Right Sized Target 

Ensure that the photographs are scaled to the correct size of at least 300 DPI (Dots Per Inch) (Dots Per Inch). Maintaining a DPI below 200 will produce illegible and unintelligible output, while retaining a DPI over 600 will increase the output file size without enhancing the quality. Thus, a DPI of 300 works well for this purpose. 

Enhance the Contrast 

Negative optical character recognition (OCR) results may be achieved by lowering the contrast. For better results throughout the OCR process, it is recommended that you first adjust the contrast and density. Various image processing programs are available, or you may use your scanner’s software. Try increasing the difference between the text/image and its backdrop to improve readability. 

Image Binarization 

In this process, a picture with several colors (RGB) is converted to a monochrome one. Several techniques exist, from basic thresholding to complex zonal analysis to convert a color picture into a black and white one. 

A typical initial step for most OCR engines is to convert color pictures to monochrome since they often deal with monochrome internally. When you have complete authority over this preprocessing stage, you can improve the likelihood of achieving optimal OCR results. 

The binarization of photos before sending them to the OCR engine has the added benefit of making the images smaller. Our initial top-down picture is shown as a binarized bitmap down below. 


You may also call this "rotation." To do this, the picture must be "de-skewered" so that its proportions are correct. There should be no vertical or horizontal slant to the text. Deskew the image by turning it either clockwise or counterclockwise if necessary.

One facet of data capture is the ability of optical character recognition systems to pull textual information from digital photographs or scanned documents. By providing a reliable data capture method, businesses may quicken their processes and pave the way for the automation of content processing.


If there's one thing we've learned about OCR performance, there's no easy way to get optimal results. Optimization efforts should begin with a thorough review of all provided paperwork. Knowing where your documents fall short allows you to tailor the aforementioned preprocessing processes to improve OCR precision. 

Creating a preprocessing pipeline specific to the documents you want to handle requires understanding the various preprocessing procedures. And that's why we have decided to make all of Docparser's preprocessing settings accessible to everyone who uses the tool. Our defaults are suitable for most situations, but each preprocessing stage may be modified to accommodate various documents. 

Have any concerns about optical character recognition (OCR) processing? You may either comment below or send us an email. Let us know how we can help you.

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