How to Improve OCR Accuracy With Advanced Image Preprocessing

Optical Character Recognition (OCR) innovation improved and better over the previous many years because of more expounded calculations, more CPU power and progressed AI techniques. Getting to OCR precision levels of 99% or higher is anyway still rather the special case and certainly not inconsequential to accomplish.

At solvio we figured out how to improve OCR precision the most difficult way possible and went through weeks on tweaking our OCR motor and stem ocr. On the off chance that you are amidst setting up an OCR arrangement and need to realize how to build the precision levels of your OCR motor, continue perusing…

In this article, we cover various procedures to improve OCR precision and offer our takeaways from building a top notch OCR framework for Solvio.

In the first place, Let's Define OCR Accuracy

With regards to OCR precision, there are two different ways of estimating how solid OCR is:

Precision on a person level

Precision on a word level

As a rule, the precision in OCR innovation is decided upon character level. How precise an OCR programming is on a person level relies upon how regularly a person is perceived effectively versus how frequently a person is perceived mistakenly. A precision of 99% implies that 1 out of 100 characters is questionable. While an exactness of 99.9% implies that 1 out of 1000 characters is unsure.

Estimating OCR exactness is finished by taking the yield of an OCR run for a picture and contrasting it with the first form of a similar book. You can then either tally the number of characters were identified accurately (character level exactness), or tally the number of words were perceived effectively (word level precision).

To improve word level exactness, most OCR motors utilize extra information in regards to the language utilized in a content. On the off chance that the language of the content is known (for example English), the perceived words can measure up to a word reference of every single existing word (for example all expressions of in the English language corpus). Words containing dubious characters would then be able to be "fixed" by discovering the word inside the word reference with the most elevated comparability.

In this article we will zero in on improving the precision on character level. The more exact characters are perceived, the less "fixing" on a word level is required.