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Pill Box Text Identification Using DBNet-CRNN.

Liuqing Xiang¹, Hanyun Wen¹, Ming Zhao¹

¹School of Computer Science, Yangtze University, Jingzhou 434025, China.

International Journal of Environmental Research and Public Health

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning model for recognizing text on pill boxes in complex natural scenes. The new method simplifies image processing and offers superior accuracy in text detection and recognition compared to older techniques.

Keywords:

convolutional recurrent neural network deep learning image processing natural scenes text recognition

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Natural scene image recognition is challenging due to inherent complexities.
Existing methods often require extensive image preprocessing, increasing application complexity.

Purpose of the Study:

To develop an end-to-end deep learning model for text detection and recognition in natural scenes, specifically for pill boxes.
To simplify the application of text recognition models by eliminating the need for prior image preprocessing.

Main Methods:

An end-to-end graphical text detection and recognition model was designed.
The system utilizes DBNet for text detection and a convolutional recurrent neural network (CRNN) for text recognition.
A B/S (Browser/Server) application was implemented for pill box recognition.

Main Results:

The proposed model achieved higher accuracy in text localization and recognition compared to the CTPN + CRNN method.
Experiments on 100 pill boxes validated the effectiveness of the new approach.
The method demonstrated significant improvements in both training and recognition processes.

Conclusions:

The developed deep learning model offers a more accurate and user-friendly solution for pill box text recognition in natural scenes.
Eliminating preprocessing steps enhances model simplicity and application efficiency.
This approach represents a significant advancement over traditional methods for complex scene text recognition.