Transformer Based Scene Character Detection over Low Quality Images

doi:10.13190/j.jbupt.2021-155

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (2): 124-130.doi: 10.13190/j.jbupt.2021-155

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Transformer Based Scene Character Detection over Low Quality Images

ZHANG Chongsheng^1,2, CHEN Jie¹, ZONG Ruixing¹, YANG Shuailei¹, FAN Gaojuan¹

1. School of Computer and Information Engineering, Henan University, Kaifeng 475001, China;
2. Henan Provincial Key Laboratory of Big Data Analysis and Processing, Kaifeng 475001, China

Received:2021-07-23 Published:2021-12-16

Abstract

Abstract: In order to solve the problem of character-level scene text detection and recognition under imperfect imaging conditions, a Transformer based scene character detection algorithm is proposed. Firstly, a Transformer based encoding-decoding structure is designed which takes the order of characters in the text instances into account, so as to output the position and order of sequence information of each character detection box can be output. Then, the Hungarian algorithm is used to calculate the loss of the algorithm which combines bounding box coordinates and ranking losses. Finally, through the experiments on three character-level annotated data sets, we show that under different evaluation metrics, the proposed method is able to achieve good performance on in terms of both scene character localization and recognition.

Key words: low quality scene text images, scene character detection, Transformer, scene character recognition

CLC Number:

TP391.43

ZHANG Chongsheng, CHEN Jie, ZONG Ruixing, YANG Shuailei, FAN Gaojuan. Transformer Based Scene Character Detection over Low Quality Images[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 124-130.

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Transformer Based Scene Character Detection over Low Quality Images

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