Scene Text Detection and Recognition: The Deep Learning Era (1811.04256v5)

Abstract: With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.

Scene Text Detection and Recognition: The Deep Learning Era

The paper "Scene Text Detection and Recognition: The Deep Learning Era" offers a comprehensive survey of the advancements in scene text detection and recognition, driven by deep learning methodologies. As a key area in computer vision, the extraction of textual information from natural scenes has seen significant progress due to the transformative potential of deep neural networks.

Key Contributions and Methodologies

The survey delineates the evolution of scene text detection from early attempts that leveraged hand-crafted features and multi-step processes to modern deep-learning-based approaches. Initial methods, reliant on techniques such as Connected Components Analysis (CCA) and Sliding Window (SW) classification, have given way to more integrated and efficient frameworks using Convolutional Neural Networks (CNNs).

The transition to deep learning has ushered in the development of two broad categories of detection systems:

1. Detection-Oriented Methods: Including one-stage and two-stage detectors adapted from general object detection (e.g., SSD, Faster R-CNN). These methods focus on directly localizing text instances using bounding boxes with adaptations for text-specific challenges like arbitrary orientations and aspect ratios.
2. Component-Based Approaches: Such as segment-linked methods and pixel-level models that predict sub-text components, offering flexibility in handling curved and long texts.

Recognition methodologies evolved through CTC-based and encoder-decoder frameworks, each offering unique advantages in handling sequence alignment and transcription. Recent challenges have led to innovations like spatial transformations and 2D attention mechanisms for better handling of irregular text.

Numerical Results and Benchmark Performance

The paper presents extensive numerical results on widely used benchmarks such as ICDAR, COCO-Text, and Total-Text. On these datasets, state-of-the-art methods show improved precision, recall, and F1-scores, demonstrating the capability of contemporary deep learning solutions to excel in both text detection and recognition tasks.

Emerging Trends and Future Directions

The paper highlights several key trends and future directions:

• The development of multi-lingual and large-scale datasets to support the training of more robust models.
• Exploration of synthetic data generation and semi-supervised learning to alleviate the dependency on extensive labeled datasets.
• Efficiency improvements to enable real-time processing on mobile and low-power devices.
• Better evaluation metrics that capture the true performance impact of varying detection and recognition conditions.

Conclusion

The paper acts as a valuable resource for researchers, synthesizing recent advancements and outlining the substantial changes introduced by deep learning in the field of scene text detection and recognition. The challenges and future research opportunities discussed could further unfold new directions in the pursuit of more efficient and comprehensive scene text understanding systems. The implications of such advancements extend into various practical applications, prominently in areas like augmented reality, document analysis, and autonomous navigation systems.