activity
20162021
most cited2D Attentional Irregular Scene Text Recognizer

50 citations · 68 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV20215 cited

PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network

Pengfei Wang, Chengquan Zhang, Fei Qi +7

The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based metho…

cs.CV2019

Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Minghui Liao, Pengyuan Lyu, Minghang He +3

Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and compl…

cs.CV201950 cited

2D Attentional Irregular Scene Text Recognizer

Pengyuan Lyu, Zhicheng Yang, Xinhang Leng +3

Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers. Recently, some irregular scene text recognizers either rectify t…

cs.CV2018

Scene Text Recognition from Two-Dimensional Perspective

Minghui Liao, Jian Zhang, Zhaoyi Wan +5

Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem. Though achieving excellent performance,…

cs.CV2018

Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Pengyuan Lyu, Minghui Liao, Cong Yao +2

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting,…

cs.CV2018

Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

Pengyuan Lyu, Cong Yao, Wenhao Wu +2

Previous deep learning based state-of-the-art scene text detection methods can be roughly classified into two categories. The first category treats scene text as a type of general…