Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation (1905.05980v1)

Abstract: Scene text detection attracts much attention in computer vision, because it can be widely used in many applications such as real-time text translation, automatic information entry, blind person assistance, robot sensing and so on. Though many methods have been proposed for horizontal and oriented texts, detecting irregular shape texts such as curved texts is still a challenging problem. To solve the problem, we propose a robust scene text detection method with adaptive text region representation. Given an input image, a text region proposal network is first used for extracting text proposals. Then, these proposals are verified and refined with a refinement network. Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found. In this way, text regions of arbitrary shapes are detected and represented with adaptive number of boundary points. This gives more accurate description of text regions. Experimental results on five benchmarks, namely, CTW1500, TotalText, ICDAR2013, ICDAR2015 and MSRATD500, show that the proposed method achieves state-of-the-art in scene text detection.

Comprehensive Review of "Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation"

Scene text detection remains a critical area within computer vision, providing the foundation for numerous applications, from autonomous vehicles to assistive technologies. The paper "Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation" by Wang et al. advances this domain by addressing the complexity of detecting arbitrarily-shaped text, which presents significant challenges due to irregular geometries and diverse orientations.

The authors propose a novel method that emphasizes adaptive text region representation, supported by a robust framework featuring a Text Region Proposal Network (Text-RPN) and a Recurrent Neural Network (RNN)-based refinement process. This dual-stage approach aggressively tackles the nuances associated with varied text shapes in natural scenes.

Methodology Overview

At its core, the method utilizes a Text-RPN to initiate text proposals by extracting and leveraging feature maps from input images. This is achieved using a SE-VGG16 backbone, an enhancement over traditional VGG16 via embedded Squeeze-and-Excitation (SE) blocks, facilitating improved channel-wise feature recalibration. Following the initial proposal stage, a refinement network that incorporates an RNN is employed to predict adaptive boundary points continually. Unlike the fixed-point strategies seen in other methods, the RNN halts predictions once an optimal polygon has been established, accommodating the arbitrary nature of the text shapes.

This strategy adeptly sidesteps the limitations of fixed-point models and the computational heft of pixel-wise predictions, exemplified by methods like TextSnake and Mask Textspotter, thus enhancing processing speed and computational efficiency without sacrificing accuracy.

Experimental Validation

The effectiveness of the proposed method is substantiated through rigorous evaluations across multiple leading benchmarks—CTW1500, TotalText, ICDAR2013, ICDAR2015, and MSRA-TD500. The results demonstrate its superior ability to not only accommodate multi-oriented and curved text but also execute text detection performant enough to set new benchmarks in precision and recall metrics across varied datasets.

• CTW1500 and TotalText: The paper outlines noteworthy improvements in handling curved texts, with Hmean scores reaching 80.1% and 78.5%, respectively, outperforming state-of-the-art methods like TextSnake.
• ICDAR2013 and ICDAR2015: These widely recognized datasets further underscore the method’s robustness in detecting horizontal and multi-oriented texts, with competitive results attained in both recall and precision.
• MSRA-TD500: This dataset’s unique challenge of mixed-language, long text lines was effectively managed, achieving an Hmean of 83.6%, showcasing the method's versatility.

Implications and Future Directions

Practically, the proposed method stands to benefit real-time applications in environments where text of arbitrary shapes is prevalent. Theoretically, it contributes to a more profound understanding of scene text representation challenges. The flexible point modeling offers a promising direction for future improvements in text detection sequences and end-to-end recognition strategies.

For future research, integrating corner detection techniques could further enhance model accuracy and reduce training complexity. There remains potential for developing end-to-end text recognition systems that seamlessly integrate with arbitrary shape detection models, potentially revolutionizing the efficacy of recognition systems in dynamic, text-rich environments.

In conclusion, Wang et al. substantially advance the field of scene text detection by proposing a method that facilitates precise, efficient detection of complex text shapes, highlighted by strong performance across critical benchmarks. The careful combination of adaptive learning with proven neural network architectures sets a benchmark in the ongoing effort to broaden the scope and applicability of scene text recognition technologies.