Text Detection in Images Using Alpha Gamma Parameter Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Text detection in natural world images, such as outdoor scenes, is challenging due to varying font styles, sizes, and background contrasts, making it difficult for existing methods to accurately identify and classify text within images.
Innovation Solution
A model-based approach that calculates alpha and gamma values based on character length, width, and inter-character spacing distributions to classify image regions as text-containing or not, independent of font, letter-style, and language, enabling faster and more reliable text detection with specified performance bounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text detection methods are used in natural world images, then detection can be performed, but detection accuracy deteriorates due to varying font styles, sizes, and background contrasts
Solution Approach 1:
The patent transforms the text detection problem from direct image analysis to parameter space analysis by computing statistical parameters (alpha and gamma values) that characterize text-like patterns. This parameter transformation enables the system to detect text across varying fonts, sizes, and backgrounds by comparing computed parameters against predefined text parameter ranges, thereby resolving the contradiction between detection accuracy and adaptability.
2Productivity
If model-based text detection is implemented, then detection speed improves, but computational complexity increases due to parameter calculations
Solution Approach 1:
The patent extracts only the essential statistical parameters (alpha and gamma values) needed for text detection from the full image data, discarding redundant information. This selective extraction approach reduces computational complexity compared to comprehensive image analysis methods while maintaining detection speed, as the system only computes these specific parameters rather than performing exhaustive feature analysis.
3Area of stationary object
If text detection is performed on small text footprints in large images, then comprehensive coverage is achieved, but detection reliability deteriorates
Solution Approach 1:
The patent transitions from spatial domain analysis to parameter space analysis by computing alpha and gamma values that represent text characteristics in a transformed dimension. This dimensional transformation allows the system to detect small text footprints reliably across large images, as the parameter-based approach is scale-invariant and can identify text-like patterns regardless of their size or position in the image.
Data Source
AI summary
Methods and apparatuses are provided for facilitating detection of text within an image. A method may include calculating an alpha value associated with an image region containing a hypothesized text fragment. The alpha value may be defined as a function of a curved character length distribution, a character width distribution, and an inter-character spacing distribution for the hypothesized text fragment. The method may additionally include calculating a gamma value based at least in part on an interval length distribution determined for the hypothesized text fragment. The method may also include classifying whether the image region is a text-containing region based at least in part on the calculated alpha and gamma values. Corresponding apparatuses are also provided.