The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ideal representation should be compact, complete, efficient, and reusable for subsequent recognition in our opinion. However, previous representations have flaws in one or more aspects. Thin-Plate-Spline (TPS) transformation has achieved great success in scene text recognition. Inspired by this, we reversely think of its usage and sophisticatedly take TPS as an exquisite representation for arbitrary shape text representation. The TPS representation is compact, complete, and efficient. With the predicted TPS parameters, the detected text region can be directly rectified to a near-horizontal one to assist the subsequent recognition. To further exploit the potential of the TPS representation, the Border Alignment Loss is proposed. Based on these designs, we implement the text detector TPSNet, which can be extended to a text spotter conveniently. Extensive evaluation and ablation of several public benchmarks demonstrate the effectiveness and superiority of the proposed method for text representation and spotting. Particularly, TPSNet achieves the detection F-Measure improvement of 4.4\% (78.4\% vs. 74.0\%) on Art dataset and the end-to-end spotting F-Measure improvement of 5.0\% (78.5\% vs. 73.5\%) on Total-Text, which are large margins with no bells and whistles.
翻译:近些年来,现场文字探测和识别的研究焦点已经转向任意的文字,因为文字形状代表是一个根本性问题。理想的表述方式应该是紧凑、完整、高效和可再用于我们的意见中随后的承认。然而,以前的表述方式在一个或多个方面都有缺陷。Thin-Plate-Spline(TPS)转换方式在现场文字识别方面取得了巨大成功。受此启发,我们反向地认为其使用方式,并且巧妙地将TPS转换为任意形状文本代表方式的精密代表方式。TPS代表方式是紧凑、完整和高效的。有了预测的TPS参数,所检测到的文字区域可以直接纠正为近横向区域,以协助随后的承认。为了进一步挖掘TPS代表形式的潜力,提出了边界调整损失。基于这些设计,我们实施了文本探测器TPSNet(TPSNet),可以方便地将其扩展为文本显示任意形状代表方式代表方式和定位方法的有效性和优越性。特别是,TPSNet(TPSNet)可以直接纠正F-Me-Meal-endal-ends (7.___________BAR_____Q_______________________Q_______________________________________________________________________________________________________________________________________________________________________________________________________________________________