Multiple improvement strategies for high-density text detection based on DBNet

Abstract

High-Density text detection is actually challenging task, which deserves more attention on its performance improvement. Based on a typical existing text detection model called DBNet, this paper introduces a new approach in improving the performance of it in text-rich and readability interfered scenarios, which are mostly characterized by dozens of lines of text using different font sizes and text background harder for detecting and reading, the most significant scenario is text detection on the passport information page. The approach proposed in this study includes several steps, namely a baseline setting, a method of dataset enrichment and new loss function aiming at raising text detection for short texts and single characters, such methods has achieved enhanced improvement on text detection especially on short texts: an overall precision reaching 0.7854 and recall reaching 0.8758. Compared with the base model, the experimental results demonstrated the effectiveness of the proposed methods in this study.