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9 Steps To Improve OCR Accuracy

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9 Steps To Improve OCR Accuracy

Explore these nine steps that can help you increase and improve the existing accuracy of your OCR engine.

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OCR technology has become widely popular today. Existing workflows and business processes have improved a lot after companies started adopting it. Some have even created their own versions of it to achieve better results in terms of productivity. Although, increasing OCR accuracy isn’t something which 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 keep in mind the following tips:

  1. Accuracy is achievable at a character level.
  2. Accuracy is gainable at a word level.

On the character level accuracy, an OCR capability is judged on how often it can recognize a right character, rather than how often it identifies a wrong character. Similarly, word level accuracy means how frequently an OCR identifies a right word. 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 of 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, OCR is mainly responsible to understand the text in a given image, so it’s necessary to choose the right one, which can pre-process images in a better way.

3. Scaling the Image to the Right Size

We try to scale an image to a standard size, which is around 300 dpi. Any image that 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 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 page layout to help OCR identify text boundaries in a more accurate manner.

8. Analyzing 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 a 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 that has to be scanned. Only then can one take the necessary actions to improve OCR accuracy.

Find out more about OCR solutions.

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artificial intelligence ,machine learning ,ocr ,images ,list

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