Image Inclination Angle Detection Using Character Centroid Clustering

The patent segments the document image into multiple character regions and processes each region independently to detect inclination angles. Instead of treating the entire document as a single entity, the method divides it into character blocks, detects local inclinations, and then synthesizes the overall inclination angle, thereby improving precision for documents with varied character heights and layouts.

The patent transitions from a one-dimensional regression line approach to a two-dimensional analysis by examining character distributions across multiple regions. It uses spatial coordinates of character centroids and applies geometric transformations to detect inclination angles more accurately, adding dimensional depth to the detection process.

The patent performs preliminary actions by first detecting character positions and centroids before inclination angle detection. It pre-processes the image to identify and mark character regions, which simplifies subsequent inclination detection by providing ready-to-use coordinate data, thereby reducing overall processing complexity while maintaining versatility.

The patent replaces complex mechanical edge extraction and thinning operations with a more efficient computational approach using character centroid detection and geometric transformation. Instead of manually extracting edges through multiple processing steps, it uses coordinate-based methods and mathematical transformations to achieve the same goal with reduced complexity.

The patent implements self-service by using the document's own character structures to determine the inclination angle. It extracts features directly from the document content itself (character positions, centroids, and distributions) rather than relying on external reference lines or markers, enabling accurate and rapid inclination detection that directly improves print quality.

The patent changes key parameters from traditional methods by using character centroid coordinates and spatial distributions instead of edge pixel coordinates. It transforms the inclination detection problem into a parameter-based geometric calculation using character positions, which maintains processing speed while significantly improving accuracy and resulting print quality.