Arbitrary Shape Text Detection using Transformers

• URL: http://arxiv.org/abs/2202.11221v1
• Date: Tue, 22 Feb 2022 22:36:29 GMT
• Title: Arbitrary Shape Text Detection using Transformers
• Authors: Zobeir Raisi, Georges Younes, and John Zelek
• Abstract summary: We propose an end-to-end trainable architecture for arbitrary-shaped text detection using Transformers (DETR) At its core, our proposed method leverages a bounding box loss function that accurately measures the arbitrary detected text regions' changes in scale and aspect ratio. We evaluate our proposed model using Total-Text and CTW-1500 datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for multi-oriented text.
• Score: 2.294014185517203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose an end-to-end trainable architecture based on Detection using Transformers (DETR), that outperforms previous state-of-the-art methods in arbitrary-shaped text detection. At its core, our proposed method leverages a bounding box loss function that accurately measures the arbitrary detected text regions' changes in scale and aspect ratio. This is possible due to a hybrid shape representation made from Bezier curves, that are further split into piece-wise polygons. The proposed loss function is then a combination of a generalized-split-intersection-over-union loss defined over the piece-wise polygons and regularized by a Smooth-$\ln$ regression over the Bezier curve's control points. We evaluate our proposed model using Total-Text and CTW-1500 datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for multi-oriented text, and show that the proposed method outperforms the previous state-of-the-art methods in arbitrary-shape text detection tasks.

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