How to Improve the Accuracy of OCR Translation Tools?
Optical Character Recognition (OCR) technology got better and better over the past decade thanks to more elaborated algorithms, more CPU power,r, and advanced machine learning methods. Getting to OCR translation accuracy levels of 99% or higher is however still rather the exception and definitely not trivial to achieve.
We learned at Devnagri AI the hard way how to fine-tune our OCR engine to achieve good OCR accuracy and spent weeks fine-tuning our  Image to Text Converter Online engine. So, if you are in the process of setting up an OCR solution and want to know how to increase the accuracy levels of your OCR engine, then keep on reading…
This blog talks about various techniques to enhance OCR accuracy and shares what we learn from building a world-class OCR system for Devnagri AI.
So, First, Let's Define OCR Translation Accuracy
Now when talking about OCR accuracy, there are two ways of measuring how reliable OCR is:
Accuracy on a character level: In most cases, the accuracy in OCR technology is gauged on a character level. The extent to which an OCR software is accurate on a character level is determined by how often it recognizes a character correctly and how often it recognizes a character incorrectly. A 99% accuracy means that 1 out of every 100 characters is uncertain. However, an accuracy of 99.9% means that 1 out of every 1000 characters is uncertain.
Accuracy on a word level: Most OCR translation engines use extra language knowledge about text to enhance their word-level accuracy. That is if the language is known where the text, for instance, is the English language, words recognized can then be compared to the dictionary of all extant words-for example, to all words in an English language corpus. Words containing uncertain characters can then be "fixed" by finding the word inside the dictionary with the highest similarity.
In this blog, we will focus on improving the accuracy of character level. The more accurate characters are recognized, the less "fixing" on a word level is required.
How to Increase Accuracy With OCR Translation Image Processing?
Assuming that you have already settled on an  Image to Text Converter Online engine, we're now down to one single moving part in the equation to improve the accuracy of OCR:Â
Good Quality Original Source
Yes, we're repeating this on purpose! The very first simple step in obtaining precise OCR translation conversions is guaranteeing the good quality of the source images. First off, see that the original paper document does not contain tears, creases, or fading or was printed in poor contrasting color ink. In that case, outputting won't be clear enough. Use therefore the cleanest and the most original source of the file to convert.
Scaling To The Right Size
Make sure the images are at the appropriate resolution and are usually of at least 300 DPI (Dots Per Inch). Lowering DPI below 200 results in blurry and unclear images. A higher DPI of above 600 unnecessarily inflates the output file size but fails to improve the quality of the file. Therefore, 300 DPI is appropriate in this regard.
Increase Contrast
Low contrast can lead to poor OCR Translation. Increase the contrast and density before scanning. This can be done directly in the scanning software or any other image-processing software. Increasing the contrast of the text/image with its background brings out more clarity in the output.
Binarize Image
This is a step that converts a multicolored image (RGB) into a black-and-white image. There are several algorithms for converting a color image to a monochrome image, ranging from simple thresholding to more sophisticated zonal analysis.
What We Learned From Building The Devnagri AI OCR Pipeline?
When there is one thing learned about OCR accuracy, there is no silver bullet, and nothing is easier when it comes to shortcutting  Image to Text Converter Online performance. You must inspect the documents carefully before attempting anything. Once you know your shortfalls, you can proceed to the applications of preprocessing steps as aforementioned to improve the accuracy of your OCR translation.
It is key to understanding how preprocessing works to tailor a preprocessing pipeline for the documents that you want to process. That's why we wanted to expose all preprocessing options to our users in Devnagri AI. Our default settings actually work well for most cases, but every preprocessing step can be tweaked according to the type of document that a user wants to process.
