Research on Text Detection Algorithm based on Improved FPN

Wang, Huibai; Feng, Shaoxian

doi:10.1109/iaeac54830.2022.9929716

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) 2022

DOI: 10.1109/iaeac54830.2022.9929716

|View full text |Cite

Research on Text Detection Algorithm based on Improved FPN

Huibai Wang

Shaoxian Feng

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism

Peng

Nie

2023

Electronics

View full text Add to dashboard Cite

Text recognition in natural scenes has been a very challenging task in recent years, and rich text semantic information is of great significance for the understanding of a scene. However, text images in natural scenes often contain a lot of noise data, which leads to error detection. The problems of high error detection rate and low recognition accuracy have brought great challenges to the task of text recognition. To solve this problem, we propose a text recognition algorithm based on natural scenes. First, the task of text detection and recognition is completed in an end-to-end way in a framework, which can reduce the cumulative error prediction and calculation caused by cascading, and has higher real-time and faster speed. In addition, we integrate a multi-scale attention mechanism to obtain attention features of different scale feature maps. Finally, we use the efficient deep learning network (EE-ACNN), which combines a convolutional neural network (CNN) with an end-to-end algorithm and multi-scale attention to enrich the text features to be detected, expands its receptive field, produces good robustness to the effective natural text information, and improves the recognition performance. Through experiments on text data sets of natural scenes, the accuracy of this method reached 93.87%, which is nearly 0.96–1.02% higher than that of traditional methods, and which proves the feasibility of this method.

show abstract

Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism

Peng

Nie

2023

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Research on Text Detection Algorithm based on Improved FPN

Cited by 1 publication

References 5 publications

Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism

Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism

Contact Info

Product

Resources

About