Today, OCR technology is widely known. Since companies started implementing the technology, current workflows and business processes changed significantly. Increasing the accuracy of OCR is not immediately due or in the process of achieving it, but you can certainly try to develop your own implementation to achieve better results in terms of efficiency.
Read About Most Frequently Asked Questions about OCR
There are two ways to calculate the effectiveness of an OCR: first, describe the accuracy on the character level, and second, calculate accuracy on the word level. Then, when it comes to improving OCR precision, you have two moving parts in the equation:
If the accuracy of the original source image is good and the human eye can clearly see it, the best OCR results can be obtained. However, if you are not sure you can see the original source clearly, it is likely that the OCR results would contain errors. The higher the original image quality, the easier it is to separate the characters from the rest, and the higher the OCR accuracy would be.
Different OCR engines are available, and although they all use the same algorithm, many of them have their own strengths and weaknesses. Comparing the accuracy of OCR is difficult, as it depends mainly on the allocated budget and the way you adapt your existing system to select the right OCR engine. Within the OCR engines, there are programs that actually try to recognize text in images, but they are not as accurate as the original image.
Using OCR technology for your work is quite tricky and needs time to learn from mistakes. However, OCR works like humans do when reading documents. Well, here are ways to better perform your OCR accuracy:
Before using OCR, make sure you can read the images with your own eyes. If you, with your own eyes, can’t see the image clearly, make sure the original source images are not damaged AND wrinkle-free. So, use the cleanest and most original files for better results.
OCR engine needs to read source images not only the ones with the best quality but also the right resolution. Make sure the image is resized to the correct size, which is usually about 1 / 10 of the original size (1.5 mm x 1 mm) or less. This way, the result will be more accurate.
Human eyes can’t even read documents that have many noises, so does the OCR engine. Noises make the engine difficult to read original sources and it can decrease the OCR accuracy. If the image has background or foreground noise, remove it to get a higher quality data extraction.
How do you see white papers with light grey ink? You -and the OCR engine must be uncomfortable reading such papers. Thus, try to increase the contrast between text and background brings more clarity to the output. The best contrast will help the OCR engine to read images accurately.
No one wants to read papers upside down. Thus, make sure you get the image in the right format and shape (text should appear horizontal and not inclined). The image can be rotated by tilting it to one side, turning it clockwise or counter-clockwise, and turning it back to the other side.
Human and OCR accuracy is actually the same since both of them are working in the same ways. The only difference is that OCR uses engines to get the jobs done. Choosing the right engine is critical because it depends on many aspects. If you’re looking for an OCR engine that has the best accuracy, Gleematic is the one you need. We apply advanced machine learning to enhance accuracy in OCR.
Written by Elsa Ajarwati