Deep Direct Regression for Multi-Oriented Scene Text Detection (1703.08289v1)

Abstract: In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. Then we analyze the drawbacks of the indirect regression, which the recent state-of-the-art detection structures like Faster-RCNN and SSD follows, for multi-oriented scene text detection, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial for localizing incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F1-measure of 81%, which is a new state-of-the-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance.

Deep Direct Regression for Multi-Oriented Scene Text Detection

The paper "Deep Direct Regression for Multi-Oriented Scene Text Detection," authored by Wenhao He, Xu-Yao Zhang, Fei Yin, and Cheng-Lin Liu, explores an innovative approach to detecting multi-oriented scene text through a direct regression-based methodology. This paper is significant not only for its conceptualization of object detection as a regression task but also for its practical implications in the domain of scene text detection.

Methodology Overview

This research introduces a paradigm shift by classifying object detection methodologies into direct and indirect regression models. Direct regression models predict boundary coordinates directly from given points, eschewing reliance on predefined proposals or anchors, unlike indirect regression frameworks, such as Faster-RCNN and SSD. The authors contend that these traditional indirect methods are suboptimal for multi-oriented text detection due to challenges in proposal generation and inefficiencies in handling inclined texts.

The authors propose a novel deep direct regression approach utilizing a fully convolutional network optimized end-to-end. This network uniquely outputs bi-task results: one for pixel-wise text versus non-text classification, and another for direct regression determining quadrilateral text boundaries.

Empirical Validation

In empirical tests, the proposed method demonstrated superior performance, particularly on the ICDAR2015 Incidental Scene Text benchmark, achieving an unprecedented F1-measure of 81%. Notably, this surpasses previous methods by a significant margin, reinforcing the method's efficacy. The paper also details successful application on other prominent datasets such as MSRA-TD500 and ICDAR2013, showcasing the method’s versatility.

Key Findings and Implications

1. Performance Metrics: The introduced deep regression framework outperforms indirect regression counterparts, with higher precision and recall rates, indicative of its effectiveness in various text detection scenarios.
2. Efficiency and Simplicity: The framework's efficiency is bolstered by the elimination of complex proposal mechanisms, requiring only a straightforward convolutional network and a single post-processing step, Recalled Non-Maximum Suppression (R-NMS). This results in improved system efficiency and reduced computational requirements, presenting both theoretical and practical benefits.
3. Practical Implications: The method's adeptness at handling scene text of arbitrary orientations and scales presents broad applicability in image-based text recognition challenges, such as automatic content extraction from diverse real-world scenes.
4. Theoretical Contributions: By positioning direct regression as a viable alternative to indirect methods, this paper contributes to broader object detection frameworks, prompting further exploration into regression-based approaches for various detection tasks beyond text.

Future Directions

The paper suggests future research avenues focused on enhancing both the robustness and speed of detection frameworks. This could involve leveraging advancements in deep learning architecture and optimization strategies, as well as broadening the method's applicability across different contexts and languages.

In summary, this research exemplifies a methodologically sound and empirically validated approach to multi-oriented scene text detection. Its implications extend beyond text detection, contributing significantly to ongoing discussions and developments within object detection and computer vision research communities. This paper thus represents a meaningful step forward in addressing the inherent challenges of detecting and localizing multi-oriented texts in diverse and complex scene images.