Image processing

The accuracy and reliability of text recognition is highly dependent on the quality of the original image. Aspose.OCR for Python via .NET offers a large number of fully automated and manual image processing filters that enhance an image before it is sent to the OCR engine.

Each filter increases recognition time. The approximate amount of additional time required for preprocessing (as a percentage of the minimum OCR pipeline) is listed in the Performance Impact column.

Filter Action Performance impact Usage scenarios
Skew correction Automatically straighten images aligned at a slight angle to the horizontal. 12% Skewed images
Rotation Manually rotate severely skewed images. 7.5% Rotated images
Noise removal Automatically remove dirt, spots, scratches, glare, unwanted gradients, and other noise from photos and scans. 175% extra time;
38% more memory (1)
Old books
Documents with stains and dirt
Contrast correction Automatically adjust the image contrast. 7.5% Photos
Old papers
Text on a background
Resizing Proportionally scale images up / down, or manually define the width and height of the image. up to 100% (2) Medication guides
Food labels
Full-sized photos from modern cameras and smartphones
Scanned images at very high DPI
Binarization Convert images to black and white automatically or manually adjust the criteria that determines whether a pixel is considered black or white. 0.9% Always used for text detection and most automatic image corrections
Conversion to grayscale Discard color information from images and leave only shades of gray. 0.5% Photos
Scanned ID cards
Full-color scans
Color inversion Swap image colors so that light areas appear dark and dark areas appear light. 0.25% White text on black background
Business cards
Dilation Increase the thickness of characters in an image by adding pixels to the edges of high-contrast objects, such as letters. 3.1% Receipts
Printouts with very thin font
Median filter Blur noisy images while preserving the edges of high-contrast objects like letters. 6.25% Photos taken in low light conditions
Poor quality printouts
Highly compressed JPEG’s
Dewarping Straighten page curvature and fix camera lens distortion for page photos.
This method requires significant resources and time! Consider using it only if the image has geometric distortions preventing accurate recognition.
30-40 seconds;
Up to 4 times more memory (3)
Photos of curved pages
Ultra wide-angle and fisheye photos
Photos from entry-level smartphones

Chaining processing filters

Multiple processing filters can be applied to the same image to further improve the recognition quality. The filters are applied one by one in the order they are added to PreprocessingFilter object.

Note that each filter requires additional time and resources on the computer running the application. Do not add extra filters if you are satisfied with the recognition accuracy, especially when developing web applications.

filters = PreprocessingFilter()

Approximate increase of processing time: 0%

Previewing and saving processed images

Aspose.OCR for Python via .NET offers an easy way to access or save processed images using the methods of the ImageProcessing class:

Method Return value Description
render() OcrInput Applies processing filters to all images in a batch and returns a new batch with processed images. This batch can later be submitted for recognition or used for optimization.
save() OcrInput Applies processing filters to all images in batch and saves the resulting images in the specified folder. That method also returns a new batch with processed images, that can be later submitted for the recognition.

You can use these methods to analyze the effectiveness of processing filters, exclude unnecessary filters that consume resources without affecting the result, or show the result of image processing in the user interface.

# Set processing filters
filters = PreprocessingFilter()
# Add all pages from a scanned PDF to OcrInput object and apply processing filters
input = OcrInput(InputType.PDF, filters)
# Save processed images from the provided PDF to the "images" folder, "images")