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Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale.

Yoshito Nagaoka1, Tomo Miyazaki1, Yoshihiro Sugaya1

  • 1Graduate School of Engineering, Tohoku University, Sendai 9808579, Japan.

Sensors (Basel, Switzerland)
|February 12, 2021
PubMed
Summary

This study introduces a new convolutional neural network (CNN) for intelligent sensors to improve small text detection. The novel architecture enhances feature extraction across multiple resolutions, crucial for accurate text recognition.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Intelligent sensors increasingly utilize text detection capabilities.
  • Detecting small text presents a significant challenge in current systems.
  • Existing methods struggle with scale variations in text recognition.

Purpose of the Study:

  • To propose a novel convolutional neural network (CNN) architecture for text detection.
  • To enhance the sensitivity of text detection models to varying text scales.
  • To address the limitations of current methods in small text detection.

Main Methods:

  • Extraction of multi-resolution feature maps using multi-stage convolution layers.
  • Maintaining feature size and preventing information loss during extraction.
Keywords:
convolutional neural networksmultiple scalesscene text detection

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  • Designing the CNN architecture with consideration for receptive field size for proposal generation.

    • Main Results:

    • The proposed CNN architecture demonstrates improved performance in text detection.
    • Multi-resolution feature extraction effectively preserves information for small text.
    • Experimental validation highlights the critical role of receptive field size in performance.

    Conclusions:

    • The novel CNN architecture is effective for text detection, particularly for small text.
    • Multi-resolution feature maps and controlled receptive fields are key to the model's success.
    • This research contributes to advancing intelligent sensor capabilities in text recognition.