TextMamba: Scene Text Detector with Mamba

• URL: http://arxiv.org/abs/2512.06657v1
• Date: Sun, 07 Dec 2025 05:06:19 GMT
• Title: TextMamba: Scene Text Detector with Mamba
• Authors: Qiyan Zhao, Yue Yan, Da-Han Wang,
• Abstract summary: We propose a novel scene text detector based on Mamba that integrates the selection mechanism with attention layers.<n>We adopt the Top_k algorithm to explicitly select key information and reduce the interference of irrelevant information in Mamba modeling.<n>Our method achieves state-of-the-art or competitive performance on various benchmarks.
• Score: 6.992080935409672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In scene text detection, Transformer-based methods have addressed the global feature extraction limitations inherent in traditional convolution neural network-based methods. However, most directly rely on native Transformer attention layers as encoders without evaluating their cross-domain limitations and inherent shortcomings: forgetting important information or focusing on irrelevant representations when modeling long-range dependencies for text detection. The recently proposed state space model Mamba has demonstrated better long-range dependencies modeling through a linear complexity selection mechanism. Therefore, we propose a novel scene text detector based on Mamba that integrates the selection mechanism with attention layers, enhancing the encoder's ability to extract relevant information from long sequences. We adopt the Top\_k algorithm to explicitly select key information and reduce the interference of irrelevant information in Mamba modeling. Additionally, we design a dual-scale feed-forward network and an embedding pyramid enhancement module to facilitate high-dimensional hidden state interactions and multi-scale feature fusion. Our method achieves state-of-the-art or competitive performance on various benchmarks, with F-measures of 89.7\%, 89.2\%, and 78.5\% on CTW1500, TotalText, and ICDAR19ArT, respectively. Codes will be available.

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