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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Attention Guided Feature Encoding for Scene Text Recognition.

Ehtesham Hassan¹, Lekshmi V L¹

¹Department of Computer Science and Engineering, Kuwait College of Science and Technology, Doha District, Block 4, Kuwait City 35004, Kuwait.

Journal of Imaging

|October 26, 2022

Summary

This study introduces a new convolutional recurrent neural network for scene text recognition, improving accuracy on diverse real-world images. The method enhances feature extraction for better text recognition in computer vision.

Keywords:

LSTM convolutional neural network recurrent neural network scene text recognition

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Scene text recognition is challenging due to variations in text appearance (shape, size, font).
Existing methods often struggle with complex real-world image conditions.

Purpose of the Study:

To develop an improved scene text recognition methodology using a novel feature-enhanced convolutional recurrent neural network.
To address sequence-to-sequence modeling challenges in text recognition from images.

Main Methods:

Proposed a deep encoder-decoder network with a hierarchical convolutional encoder.
Incorporated spatial attention blocks within the encoder for focused feature extraction.
Utilized bidirectional long short-term memory layers in the network architecture.

Main Results:

The proposed architecture effectively learns robust text-specific feature sequences.
Spatial attention guides feature extraction towards crucial textual details.
Demonstrated improved performance over state-of-the-art methods on benchmark datasets (ICDAR2013, ICDAR2015, IIIT5K, SVT).

Conclusions:

The novel convolutional recurrent neural network architecture significantly enhances scene text recognition.
The spatial attention mechanism is key to capturing relevant text features in complex scenes.
The approach offers a robust solution for real-world text recognition tasks.