Text-Attentional Convolutional Neural Networks for Scene Text Detection (1510.03283v2)

Abstract: Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

An Analysis of the Text-Attentional Convolutional Neural Network for Scene Text Detection

The paper under consideration presents a sophisticated approach to scene text detection with the introduction of the Text-Attentional Convolutional Neural Network (Text-CNN). This research builds upon the established capabilities of Maximally Stable Extremal Regions (MSERs) and the strengths of CNNs to deliver a robust mechanism for differentiating text from background in complex images. The innovative aspects of this work lie in its multi-level supervised design, novel CE-MSER detector, and the integration of a deep multi-task learning paradigm within the Text-CNN to enhance text feature computation and detection accuracy.

Overview of Contributions

Key contributions of this paper revolve around three primary areas:

1. Text-Attentional Convolutional Neural Network (Text-CNN): The Text-CNN transcends conventional CNN approaches by incorporating multi-level supervised information (text region mask, character label, and binary text/non-text classification) to enhance feature extraction specifically for text components. This tackles the issue of background noise diluting feature relevance—a limitation prevalent in prior models.
2. Contrast-Enhanced Maximally Stable Extremal Regions (CE-MSERs): By introducing a contrast-enhancement mechanism, the CE-MSERs serve as a novel low-level feature detector that capitalizes on intensity contrast between text and background, thus addressing the inherent limitations of MSERs when confronted with ambiguous text patterns.
3. Multi-task Learning Framework: Training the Text-CNN as a multi-task learning problem aligns learning objectives across different levels of text feature abstraction, allowing for more nuanced learning outcomes. This is crucial in capturing both fine-grained and global text characteristics, resulting in increased robustness in text detection applications.

Numerical Results and Analysis

The proposed approach demonstrates marked improvements on standard benchmarks, exemplified by an F-measure of 0.82 on the ICDAR 2013 dataset, which outpaces the prior state-of-the-art results. The enhancements in both precision and recall metrics, with precision reaching up to 0.93, underscore the utility of incorporating detailed supervised training signals and a focus on text-specific regions in the CNN architecture. Furthermore, CE-MSERs proved to be salient in maintaining high recall levels, such as the reported recall improvement to 0.74 from baseline MSER methodologies.

Implications and Future Directions

The implications of deploying a multi-faceted approach like the Text-CNN extend into practical deployments of OCR, automated content indexing, and enhanced visual assistance systems. The resilience against complex backgrounds promises viability in real-world applications where variability in text presentation and environmental conditions persist.

Looking forward, this research opens avenues for further improvements and explorations in AI. Potential directions include:

• Adapting the Network for Multilingual Contexts: Extending the network's applicability to capture characters from diverse linguistic scripts within a single architectural framework.
• Generalization to Other Image-Based Tasks: The multi-tasking framework offers a template for crafting CNN architectures to solve similar hard-to-distinguish object classification tasks beyond text detection.
• Integration with Transformer Models: Further refinement could involve hybridizing this approach with transformer-based architectures to leverage temporal and contextual dependencies in scene text sequences.

This paper progresses the field of scene text detection and provides a valuable reference point for researchers seeking to optimize deep learning models for complex image interpretation tasks.