#5820. Deformable scene text detection using harmonic features and modified pixel aggregation network

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Signal Processing;
Software;
Computer Vision and Pattern Recognition;
Artificial Intelligence;
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Abstract:
Although text detection methods have addressed several challenges in the past, there is a dearth of effective methods for text detection in deformable images, such as images containing text embedded on cloth, banners, rubber, sports jerseys, uniforms, etc. This is because deformable regions contain surfaces of arbitrarily shapes, which lead to poor text quality. This paper presents a new method for deformable text detection in natural scene images. It is observed that although the shapes of characters change in a deformable region, the pixel values and spatial relationship between the pixels do not change. This motivated us to explore extraction of Maximally Stable Extremal Regions (MSER) in an image in which pixels that share common features are grouped into components. The unique character shape variations led us to explore harmonic features to represent the component shape variations, using which a classifier classifies text and non-text components from the output of the MSER step.
Keywords:
Deep learning models; Deformable text detection; Fourier harmonic features; Maximally stable extremal region; Natural scene text detection; Pixel aggregation network

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