STELA: A Real-Time Scene Text Detector With Learned Anchor

Deng, Linjie; Gong, Yanxiang; Lu, Xinchen; Lin, Yi; Ma, Zheng; Xie, Mei

doi:10.1109/access.2019.2948405

Cited by 26 publications

(16 citation statements)

References 47 publications

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“…To alleviate this problem, anchor-based methods have been proposed to enhance text localization performance. Deng et al [16] proposed a real-time scene text detector using learner anchors. The method detects the location of an object of any shape by using learned proposals.…”

Section: A Regression/anchor-based Methodsmentioning

confidence: 99%

A New Deep Wavefront Based Model for Text Localization in 3D Video

Nandanwar

Shivakumara

Ramachandra

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods.

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Section: A Regression/anchor-based Methodsmentioning

confidence: 99%

A New Deep Wavefront Based Model for Text Localization in 3D Video

Nandanwar

Shivakumara

Ramachandra

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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“…In the task of pedestrian detection, Fang et al found that geometric constraints of pedestrians can be combined with anchor frame information, and anchors containing geometric constraints are used to reduce reasoning time and reduce the error rate of reasoning in pedestrian detection [ 52 ]. Deng et al used the learned proposals in the two-stage R-CNN to propose a learnable anchor and replaced the learnable anchor with the fixed anchor in the one-stage to achieve real-time scene text detection [ 53 ]. In the object detection task of optical remote sensing images, BAO et al proposed two regressions of anchors that adapt to different IOU thresholds according to the different needs of recognition and positioning [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

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“…Scene text detection is the process of accurately localizing text instances in wild images; it is an essential component that enables various practical applications such as text recognition, blind navigation, and topological mapping to name a few [1,2]. While recent text detection methods [3][4][5][6][7][8][9] have shown reliable performance on horizontal and multi-oriented text, accurate detection of texts in an arbitrary geometric layout is still an open-ended problem.…”

Section: Introductionmentioning

confidence: 99%

Arbitrary Shape Text Detection using Transformers

Raisi¹,

Younes²,

Zelek³

2022

Preprint

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Recent text detection frameworks require several handcrafted components such as anchor generation, nonmaximum 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-overunion 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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STELA: A Real-Time Scene Text Detector With Learned Anchor

Cited by 26 publications

References 47 publications

A New Deep Wavefront Based Model for Text Localization in 3D Video

A New Deep Wavefront Based Model for Text Localization in 3D Video

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Arbitrary Shape Text Detection using Transformers

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