Today, OCR technology has become broadly famous. Since businesses began implementing it, current workflows and business processes have changed a lot. To produce better results in terms of efficiency, some have even developed their own implementations of it. While increasing the accuracy of the OCR is not something that can be achieved instantly, but in due time, one can certainly try to do so.
There are two ways of calculating how effective OCR is:
You essentially have two moving parts in the equation when it comes to improving OCR precision.
If the accuracy of the original source picture is good, that is, if the human eyes can clearly see the original source, good OCR results can be obtained. But if the original source itself is not clear, then it is most likely that OCR findings would contain errors. The higher the original source picture quality, the simpler it is to separate characters from the rest, the greater the OCR accuracy would be.
2. The Engine of OCR
The program that actually tries to recognize text in whatever picture is presented is an OCR engine. Different OCR engines, from free open source OCR engines to proprietary solutions with a heavy price tag, are available.
While many OCR engines use the same kind of algorithms, they each have their own strengths and weaknesses. Comparison of OCR precision is challenging because it mainly depends on the particular use-case, the allocated budget and how it fits with an existing system to select the correct OCR engine.
Make sure you do not damage, wrinkle, discolor or print the original paper document with low contrast ink. If so, the output will not be very clear. So, use the cleanest and most original file source that you want to convert.
Ensure that the images are scaled to the right size which usually is of at least 300 DPI (Dots Per Inch). Not less than 200 and more than 600.
Increasing the contrast between the text/image and its background brings out more clarity in the output.
If an image has background or foreground noise present in it, be sure to remove it so that we get high-quality data extraction.
Referred as rotation, this means de-skewing the image to bring it in the right format and right shape. The text should appear horizontal and not tilted in any angle. If the image is skewed to any side, deskew it by rotating it clockwise or anti clockwise direction.