Enhancing Hand Sign Recognition in Challenging Lighting Conditions Through Hybrid Edge Detection

Edge detection is essential for image processing and recognition. However, single methods struggle under challenging lighting conditions, limiting the effectiveness of applications like sign language recognition. This study aimed to improve the edge detection method in critical lighting for better sign language interpretation. The experiment compared conventional methods (Prewitt, Canny, Roberts, Sobel) with hybrid ones. Project effectiveness was gauged across multiple evaluations considering dataset characteristics portraying critical lighting conditions tested on English alphabet hand signs and with different threshold values. Evaluation metrics included pixel value improvement, algorithm processing time, and sign language recognition accuracy. The findings of this research demonstrate that combining the Prewitt and Sobel operators, as well as integrating Prewitt with Roberts, yielded superior edge quality and efficient processing times for hand sign recognition. The hybrid method excelled in backlight at 100 thresholds and direct light conditions at a threshold of 150. By employing the hybrid method, hand sign recognition rates saw a notable improvement of the pixel value of more than 100% and hand and sign recognition also improved up to 11.5%. Overall, the study highlighted the hybrid method's efficacy for hand sign recognition, offering a robust solution for lighting challenges. These findings not only advance image processing but also have significant implications for technology reliant on accurate segmentation and recognition, particularly in critical applications like sign language interpretation.