Recent segmentation techniques for scene text detection have attracted significant attention for their ability to flexibly handle texts of various shapes and orientations. These techniques benefit from the high extensibility of pixel-level representations, but they face challenges due to complex network designs and slow post-processing steps, which hinder inference speeds and lead to suboptimal detection of unusual text shapes. We present a real-time detector for text of arbitrary shapes, named the Multi-Level Feature Fusion and Pixel Clustering (MFFPC) Network, to address these challenges. This method utilizes a lightweight feature extraction network, enhanced with specially designed Feature Enhancement Module (FEM) and Feature Filter Module (FFM) to improve feature representation. MFFPC enhances visual context understanding and refines lower-level feature maps using high-level features, effectively modeling text through a lightweight segmentation head and GPU-accelerated parallel post-processing. Additionally, an auxiliary training branch, inspired by clustering algorithms, further increases segmentation accuracy. The performance of MFFPC on three benchmark datasets validates its effectiveness. Specifically, on the challenging Total-Text dataset, it achieves an F-measure of 88.7% and processes at a speed of 66.8 frames per second (FPS).