Shape Robust Text Detection with Progressive Scale Expansion Network

Paper: arXiv

by Xiang Li, Wenhai Wang, Wenbo Hou, Ruo-Ze Liu, Tong Lu, Jian Yang

DeepInsight@PCALab, Nanjing University of Science and Technology.

IMAGINE Lab@National Key Laboratory for Novel Software Technology, Nanjing University.

Introduction

Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.

Code are coming soon.

Figure 1: Illustration of our overall pipeline.

Figure 2: The procedure of progressive scale expansion algorithm.

Performance

ICDAR 2015

Method	Precision (%)	Recall (%)	F-measure (%)
PSENet-4s	87.98	83.87	85.88
PSENet-2s	89.30	85.22	87.21
PSENet-1s	88.71	85.51	87.08

ICDAR 2017 MLT

Method	Precision (%)	Recall (%)	F-measure (%)
PSENet-4s	75.98	67.56	71.52
PSENet-2s	76.97	68.35	72.40
PSENet-1s	77.01	68.40	72.45

SCUT-CTW1500

Method	Precision (%)	Recall (%)	F-measure (%)
PSENet-4s	80.49	78.13	79.29
PSENet-2s	81.95	79.30	80.60
PSENet-1s	82.50	79.89	81.17

ICPR MTWI 2018 Challenge 2

Method	Precision (%)	Recall (%)	F-measure (%)
PSENet-1s	78.5	72.1	75.2

Results

Figure 3: The results on ICDAR 2015, ICDAR 2017 MLT and SCUT-CTW1500