How To Improve The OCR Accuracy

OCR technology has become widely popular today. Existing workflows and business processes have improved a lot after companies started adopting them. Some have even created their own versions of it to achieve better results in terms of productivity. Although, increasing the OCR accuracy isn’t something that can be done overnight but one can definitely try to do so in due course of time.

So how can someone fine-tune their Optical Character Recognition engines gradually? Well, there are different ways to attain this goal. We should keep in mind the following tips:

  • Accuracy is achievable at a character level.
  • Accuracy is achievable at a word level.

On the character level accuracy, an OCR capability is judged on how often it can recognize the right character, rather than how often it identifies a wrong character. Similarly, word-level accuracy means how frequently an OCR identifies the right word and different accuracy levels for the different kinds of documents scanned, but we make it a point to achieve at least a minimum of 70% accuracy.

To increase the existing accuracy of our OCR engine we follow the below steps:

1. Checking the Source Image Quality:

Our experts make sure that the original source image is visible enough so that they can get better OCR results. There’s no point in scanning a hazy image in the first place. OCR should be able to recognize high contrasts, character borders, pixel noise, and aligned characters.

2. Choosing the Best OCR Engine:

As we all know that OCR is mainly responsible to understand the text in a given image, so it’s necessary to choose the right one which can preprocess images in a better way. Our software does a good job at that. Still, we keep updating it every now and then to make the result more accurate.

We try to scale an image to a standard size which is around 300 dpi. Any image which is lower than this size will give an unclear result, while images above 600 dpi will make the output file bigger without much quality.

4. Enhancing the Contrast of Images:

Contrast and density are vital factors to consider before scanning an image in the OCR. We process the image to enhance these factors to get clearer outputs.

5. Removing Noise from the Images:

If an image has background or foreground noise present in it, we make it a point to remove it so that we get high-quality data extraction.

6. Preparing and Handling the Document Properly:

We make sure that documents of any size can be loaded into the scanners. Also, our capture software reduces the document preparation time after they’ve been fed into these scanners.

7. Deskewing and Analyzing Page Layout:

In the preprocessing stage, it’s important to deskew the pages so that the word lines are horizontal. We try to reduce the complexity of the page layout to help the OCR identify text boundaries in a more accurate manner.

8. Analyzing the Character Edge:

The capture tool and the Optical Character Recognition software must be able to optimize the character edge so that there’s minimal labor required while extracting results.

9. Using Filters, Databases, and Thesaurus:

Extra care should be taken to reduce errors. That’s why we use language filters, databases, and thesaurus so that the extracted results make sense and don’t need further inspection.

We keep trying and testing new ways to achieve a more accurate result post-extraction. However, it’s not an overnight process, it takes a thorough understanding of the preprocessing steps to gain momentum. At first, it’s very important to know the defects of the document which has to be scanned. Only then can one take the necessary actions to improve OCR accuracy.

Originally published at https://www.infrrd.ai.

Infrrd has been offering AI as a Service since inception. Their focus is on developing faster Enterprise AI platform using AI, ML & NLP- https://infrrd.ai