Abstract:
This research introduces an innovative strategy by merging a multi-scale attention
mechanism with the OpenCV framework to enhance text detection. OpenCV, a
foundational computer vision library, excels in image preprocessing and feature
extraction [1]. Despite emerging deep learning frameworks, OpenCV's prowess in
addressing complex text detection scenarios remains limited. To address this, a multiscale
attention mechanism is proposed, enabling the model to decode text features
across diverse scales and contexts [2]. This approach improves text detection and
recognition, particularly in complex scenes, demonstrated through comprehensive
experiments on benchmark datasets [3]. Results highlight its superiority over
conventional OpenCV methods, enhancing text-related tasks and bolstering real-time
applications [4]. This integration advances text detection by combining OpenCV's
processing abilities with a multi-scale attention mechanism, aligning with OCR
frameworks such as Tesseract OCR for recognition [5]. The method's potential is
underscored in a text-focused technological landscape.
