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7 Essential Document Image Cleanup Features Your Application Needs

Document image cleanup is a vital step in building an efficient and accurate processing workflow. In a perfect world, every file an organization receives would be in pristine, high-resolution condition so it could be processed quickly and easily. Unfortunately, the reality is that documents come in all sizes, conditions, and formats. Companies can receive vital information in the form of email, traditional mail, fax, or even text. Documents scanned into a crooked, low-resolution file are just as likely to be received alongside digital versions submitted entirely through a web application.

This poses a significant challenge for software developers building the next generation of automation solutions. Without some way of cleaning up document images, companies that still rely upon manual processes will struggle to read and process files. More importantly, poor image quality interferes with optical character recognition (OCR) engine accuracy, making more human interaction necessary to verify recognition results. By integrating document image cleanup tools into their applications, developers can enhance the speed and accuracy of their automated processes and help their customers leverage the full potential of digital transformation.

7 Essential Document Image Cleanup Features Your Application Needs

There are a few essential document image cleanup tools that should be considered absolutely essential for any application that has to manage multiple file formats. To see these tools in action and understand why they’re so vital, let’s take a look at how these features work in ImageGear, Accusoft’s powerful document and image processing SDK integration.

1. Despeckling

Speckles can appear on document images for a variety of reasons. In some cases, they are unwanted image noise created during the original scanning process (the classic “salt and pepper” noise), but in other instances, they’re simply the result of dust particles on the surface of a scanned document or on the scanner itself. They are frequently encountered when converting old documents into digital form. Speckling not only interferes with OCR engine performance, but can also make it difficult to maintain image fidelity when compressing or converting files. 

ImageGear can reduce or eliminate speckling as part of the document image cleanup process. There are two ways to approach speckle removal:

  • Despeckle Method: This function removes color noise from 1-bit images by taking the average color value in a square area around the speckle and replacing its pixels with that value.
  • GeomDespeckle Method: This function uses the Crimmins algorithm to send the image through a geometric filter, reducing the undesired noise while preserving edges of the original image. This process is applied only to 8-bit grayscale images.

2. Image Inversion

With so many documents being scanned, converted, and transferred between applications, there’s a greater likelihood of something going wrong along the way. One of the most frequent problems is image inversion, which swaps pixel colors and turns a standard white background with black text into a black background with white text. This mix-up can render documents completely unreadable by OCR engines.

ImageGear can be configured to automatically recognize when image inversion is necessary. The invert method can also be used to immediately change the color of each pixel contained in the entire image, turning white to black and black to white.

3. Deskewing

Skewed document images are both cumbersome to manage and challenging for OCR engines to read accurately. Unfortunately, manually scanned documents are often uneven, and the problem is only becoming worse now that many people are using their phone cameras as makeshift document scanners. That’s why the first step in the document image cleanup process is often deskewing, which rotates and aligns the images to enhance recognition accuracy.

The deskewing process often involves more than just rotating a document, especially where images taken by a digital camera are concerned. ImageGear’s 3D deskew feature corrects for perception distortion, which can occur whenever a document is scanned by a handheld camera, using a sophisticated algorithm.

4. Blank Page Detection

Many documents converted into digital format contain information on both sides. If they are fed into a scanner along with single page documents, the resulting file will contain multiple blank pages. This might not seem like much of a problem, but if there is enough speckling or noise around the edge of the image, an application may try to apply an OCR engine to it and generate an error result. Blank page detection can quickly identify any image that is blank or mostly white and flag it for deletion.

5. Line Removal

Although they may not seem very troublesome at first glance, lines can create a number of problems for OCR engines. When lines and printed text overlap, it can be difficult for the engine to distinguish between the two. In some instances, the engine may even misread a line as a letter or number. Removing lines from a document prior to OCR reading ensures that the remaining text will be recognized more quickly and analyzed more accurately.

ImageGear supports both solid line removal and dotted line removal. The first method automatically detects and removes any horizontal and vertical lines contained in the document (like frames or tables), while the second method determines which dotted lines to remove by measuring the number and diameter of dots.

6. Border Removal

When scanned documents don’t align properly with the boundaries of the scanner or were copied onto paper that was larger than the original image at some point, the remaining space is often filled in with black. These borders are not only unsightly, but they also interfere with other document image cleanup processes. Although they can usually be cropped out easily, the cropping process alters the proportions of the image, which could create more problems later.

Removing these large black regions is easy with ImageGear’s CleanBorders option. It focuses on the areas near the edge of the page, which typically should not contain any important image data. 

7. Remove Hole Punches

Important documents were often stored in binders before they were prepared for digitization. When scanned, the blank space from the hole punch leaves a large, black dot along the edge of the document. Unfortunately, these holes sometimes overlap with text or could be picked up as filled-in bubbles by an optical mark recognition (OMR) engine.

ImageGear can identify and remove punch holes created by common hole punchers, including two, three, and five hole configurations. The RemovePunchHoles method can be adjusted to account for differing hold diameters in addition to different locations.

Unlock Your Application’s Document Image Cleanup Potential with ImageGear

Although ImageGear can perform a variety of document handling functions such as viewing, conversion, annotation, compression, and OCR processing, its document image cleanup capabilities help applications overcome key content management challenges and enhance performance in other areas. Improved document image quality allows data to be extracted more quickly, enhances the viewing experience, and reduces complications when it comes to file compression and conversion.

Learn more about the ImageGear collection of SDKs to discover how they can deliver versatile document and image processing to your applications.