Adaptive Binarization and Background Filtering

FlexiCapture Engine, FineReader Engine, Cloud OCR SDK
8.x, 9.x, 10, 11
Technology & Features

Prior to analysing the structure of the document and identifying its blocks, an OCR program will binarize the image, i.e.: it will convert a colour or a greyscale image into a monochrome one (1 bit).
Modern documents will often include complex design elements as textures and background images. If an OCR program would use only a very simple binarisation there will be too many excess dots left around the characters, which will have an adverse effect on the quality of recognition.

The same is true about binarising background images. Therefore it is crucial that the program can separate the text from the underlying textures and images. To solve this issue ABBYY technologies use two pre-processing procedures Intelligent Background Filtering and Adaptive Binarization

Intelligent Background Filtering

Intelligent Background Filtering (IBF) allows the program to separate text strings from any background, however complex, and the latter selects optimal binarization parameters for each region. Moreover, once the document has been treated with IBF, the lower-level objects such as text blocks and tables on pages with complex layouts can be detected more accurately.

Intelligent Background Filtering at work:

Adaptive Binarization

The presence of background images or textures is not the only factor that can impair recognition quality. Low recognition quality brings also the low contrast of the original document and the changing brightness of the background. For such documents the Adaptive Binarization procedure is used. It measures the brightness of the background and the saturation of the black areas along the line in order to find optimal binarization parameters for each separate line's fragment. As a result, the lines and words will be correctly detected and higher recognition accuracy will be reached.

  • Adaptive Binarization at work

Adaptive Binarization General

Adaptive Binarization - impact on characters

The following image shows why proper binarization is important for good OCR results:

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