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A Fast and Robust Text Spotter.

Siyang Qin¹, Roberto Manduchi¹

¹Computer Engineering Department, University of California, Santa Cruz.

Proceedings. IEEE Workshop on Applications of Computer Vision

|September 13, 2016

Summary

This summary is machine-generated.

This study presents an efficient text detection and localization algorithm achieving state-of-the-art results. The method uses multi-channel Maximally Stable Extremal Regions (MSERs) and deep networks for accurate word spotting.

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Accurate text detection and localization are crucial for various applications.
Existing methods often face challenges with computational efficiency and performance.

Purpose of the Study:

To develop a computationally efficient algorithm for text detection and localization.
To achieve state-of-the-art performance on standard benchmarks.

Main Methods:

Utilizes multi-channel Maximally Stable Extremal Regions (MSERs) for initial region detection.
Employs a clustering approach for region subsampling.
Binarizes representative regions and processes them through a deep network.
Incorporates a final line grouping stage for word-level segmentation.

Main Results:

Achieved F-scores of 82% on ICDAR 2011 and 83% on ICDAR 2015 benchmarks.
Operates at a computational cost of 1.2 seconds per frame.
A faster variant demonstrates only a slight performance reduction.

Conclusions:

The proposed algorithm offers a balance of high performance and computational efficiency for text spotting.
The system is suitable for real-time applications requiring accurate text localization.