Scene Text Detection with Big Bang Crunch Optimized Deep Learning

Scene text detection, a fundamental task in computer vision and document analysis, plays a pivotal role in extracting valuable information from images and videos. This paper introduces an innovative approach to scene text detection by harnessing the power of the Big Bang Big Crunch Optimization (BBBCO) algorithm. In this paper proposed BBBCO and its application to scene text detection. BBBCO's capacity to efficiently explore the feature space and adapt to variations in text size, orientation, and background complexity is a significant asset in addressing the inherent challenges of this task. By incorporating BBBCO, focused on to enhance the accuracy and robustness of scene text detection systems, particularly in scenarios where traditional methods may falter. This paper presents experimental results that demonstrate the effectiveness of BBBCO in improving text region localization and segmentation. To evaluate the algorithm on benchmark datasets and compare its performance with existing scene text detection techniques. The results underscore BBBCO's potential to push the boundaries of text detection accuracy, making it a valuable addition to the toolkit of researchers and practitioners in computer vision and document analysis. In conclusion, the fusion of BBBCO with scene text detection represents a promising avenue for advancing the state of the art in text detection and recognition. This paper sheds light on the capabilities of BBBCO and its potential to address the evolving challenges of scene text detection in real-world applications, thereby contributing to the broader field of computer vision and document analysis.