  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.
