Detecting Multi-Oriented Text with Corner-based Region Proposals

Abstract: Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn

• The paper introduces a corner-based Region Proposal Network (CRPN) that adapts to various text orientations without relying on fixed anchors.
• It employs Dual-RoI Pooling to incorporate data augmentation directly into the detection framework, enhancing feature representation and model robustness.
• Experimental results on ICDAR and COCO-Text benchmarks demonstrate improved accuracy and competitive processing speeds at 9.1 fps.

Detecting Multi-Oriented Text with Corner-based Region Proposals

The paper "Detecting Multi-Oriented Text with Corner-based Region Proposals" introduces a novel approach for detecting multi-oriented text in scene images, an area of significant importance in computer vision. Unlike traditional methods reliant on manually defined sliding windows or pre-set anchors, this research advocates a corner-based strategy to generate adaptive geometrical proposals for robust, accurate detection.

Methodology Overview

The authors propose a two-stage, region-based detection framework. In the initial stage, they employ a corner-based Region Proposal Network (CRPN) which identifies potential text locations by detecting and linking corners, as opposed to utilizing default anchors. This is a departure from conventional methods which often struggle with text instances of varying orientations and aspect ratios. Quilaterals, constructed from these linked corners, provide a flexible means for capturing text regions irrespective of their alignment.

Furthermore, the study introduces Dual-RoI Pooling, an innovation that integrates data augmentation within the region-wise subnetwork. This approach enhances the classification and regression capabilities by effectively utilizing corner proposals, and sidesteps issues commonly associated with external image transformations. As a result, the system assumes improved robustness without significant computational overhead.

Experimental Results

The efficacy of the proposed method is demonstrated through evaluations on prominent benchmarks such as ICDAR 2013, ICDAR 2015, and COCO-Text. The results manifest that the corner-based approach outperforms various state-of-the-art techniques, especially for scenes with multi-oriented text. Significant metrics include an F-measure of 0.876 on ICDAR 2013, 0.845 on ICDAR 2015, and 0.591 on COCO-Text. These outcomes underline its potential applicability in diverse real-world contexts. Additionally, the system exhibits competitive processing speeds, running at 9.1 fps.

Key Contributions

1. Corner-based Region Proposal Network (CRPN): Bypassing the reliance on manually designed anchors, CRPN links corners to form quadrilateral proposals conducive to high recall and precision in text detection tasks.
2. Link Direction Variable: This innovative element mitigates negative linkages within corner detection, crucial for distinguishing adjacent text elements within close proximity.
3. Dual-RoI Pooling: By embedding data augmentation directly into the architecture, the method enhances feature representation and model robustness, optimizing the use of training data and expanding towards a more efficient learning process.

Implications and Future Directions

This study makes significant strides in advancing multi-oriented text detection. The CRPN demonstrates a promising shift from traditional anchor-based systems, providing an adaptive mechanism that could potentially be extended to various object detection tasks beyond text. The implication of these findings can have profound impacts on applications involving optical character recognition, multilingual translation, and contextual image retrieval in complex environments.

In terms of future directions, strengthening the model with more sophisticated network architectures like ResNet or DenseNet may further enhance performance. Additionally, the approach paves the way for integration into comprehensive text recognition pipelines, setting the stage for complete end-to-end reading systems that could revolutionize interactions with visual data in dynamic settings. The paper succeeds in expanding the discourse on region-based text detection methodologies and sets a compelling foundation for continued exploration and refinement.