Image Processing
Get Optimal Data Extraction with AI-Accelerated Image Processing
Discover Why Grooper has Industry-Best Image Processing
Image processing improves document images displayed to users throughout data document capture and creates beautiful documents for permanent archival.
Image Processing Improves OCR
All OCR engines perform their best when analyzing an image free of non-text objects like black borders, logos, specs, lines, dark backgrounds, checkboxes, and etc. Grooper has over 70 image processing features to remove these objects with unprecedented accuracy and control.
High-Quality Halftone Removal
Halftone patterns are the imperfections caused by legacy image processing software poorly converting gray background regions on the page to black and white using dithering algorithms. The leftover imperfections create massive errors in OCR and data recognition, often rendering the text in these areas completely illegible.
Grooper knows how to remove imperfections from halftone regions without falsely destroying small text characters like periods and commas found elsewhere on the image.
Brilliant Border Removal
Borders are commonly very tricky to remove when a black border does not extend to the edge of the page.
However, Grooper understands how to address a variety of complex borders and cleanly removes them without inadvertently removing nearby text characters.
Photoshop-Like Inpainting
Grooper leverages full-color document images for incredible object removal and editing.
Grooper digitally restores damaged or unknown parts of an document image with information from nearby pixels.
Pixel-Perfect Line Detection & Removal
For humans, lines are needed to provide visual cues to make documents easier to read. Lines of all sizes are common and frequent in forms, tables, and fill-in-the- blanks.
However, these lines mistakenly read by OCR engines as letters or numbers. But Grooper easily removes these lines. Here’s how:
Grooper removes lines by using a precise, pixel-by-pixel mask of lines rather than a generic point-to-point/thickness approach. This method leaves nothing behind, providing a cleaner OCR image.
Grooper knows the difference between lines and data that resembles lines. It will remove lines but preserve text such as “L, l, I, and 1”.
This approach works well with short lines, even lines that are smaller than letters on documents.
Any letters or numbers that happen to be connected to lines are found and saved, in order to keep valuable data for your OCR process.