Dot matrix display character recognition method based on color features

By employing a color feature-based text recognition method for dot matrix displays, and utilizing dual-strategy filtering and K-Means clustering algorithms, the problem of character recognition in complex environments on dot matrix displays is solved, generating high-contrast masks and achieving high-precision character recognition.

CN120976940APending Publication Date: 2025-11-18SHANGHAI ADVANCED AVIONICS
0 Cites 0 Cited by

Patent Information

Application Number
CN202511311503.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-15
Publication Date
2025-11-18

AI Technical Summary

Technical Problem

Traditional OCR methods face challenges when recognizing characters on dot matrix displays, such as jagged edges on characters, uneven brightness due to changes in ambient lighting, and halo effects. These issues make character recognition difficult, and the methods also ignore color characteristics, making it difficult to effectively segment character areas in complex lighting environments.

Method used

A color feature-based text recognition method for dot matrix displays is adopted. Non-target pixels are removed through a dual-strategy filtering mechanism, color clusters are quantized using the K-Means clustering algorithm, and target colors are matched by Euclidean distance to generate a high-contrast foreground mask for use by the OCR engine.

Benefits of technology

It effectively isolates target characters, reduces background interference, and generates a high-contrast, high-fidelity foreground mask, improving the accuracy and robustness of character recognition and adapting to different devices and ambient lighting changes.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a dot matrix display character recognition method based on color features, and the method comprises the steps: S1, receiving an original image and a region of interest as input, and removing non-target pixels in the image through background filtering; s2, the filtered effective pixels are sent to a K-Means clustering algorithm for color quantization, and main color clusters in the image are automatically summarized; s3, in a color measurement matching stage, determining a target color representing a character foreground by calculating the Euclidean distance between the center of each color cluster and a preset target color in an RGB space; and S4, performing foreground mask reconstruction based on a matching result, generating a binary image only containing target character pixels, and sending the binary image to a standard OCR engine for final character recognition. According to the method, target color characters can be effectively isolated, background interference is reduced, and a foreground mask with high contrast and high fidelity is finally generated for a subsequent OCR engine to use.
Need to check novelty before this filing date? Find Prior Art