SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition (2005.10977v1)

Abstract: Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts. The semantic information is used both in the encoder module for supervision and in the decoder module for initializing. In particular, the state-of-the art ASTER method is integrated into the proposed framework as an exemplar. Extensive experiments demonstrate that the proposed framework is more robust for low-quality text images, and achieves state-of-the-art results on several benchmark datasets.

An Expert Overview of "SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition"

The field of scene text recognition (STR) has seen significant advancements with the adoption of encoder-decoder frameworks. Despite these advancements, challenges persist, particularly in dealing with low-quality images characterized by blurring, occlusions, and incomplete characters. The presented paper introduces the SEED framework, which enhances the traditional encoder-decoder approach by integrating global semantic information. This paper advocates for a semantically enriched encoder-decoder framework aimed at improving the recognition of low-quality scene texts by leveraging semantic information throughout the recognition process.

Problem Definition

Traditional encoder-decoder frameworks for scene text recognition primarily focus on local visual features, thereby neglecting global semantic cues which may better inform the recognition task. This oversight poses a problem when these systems attempt to interpret text from low-quality images, struggling to compensate for factors like image blur and incomplete characters.

Proposed Framework: SEED

The SEED framework addresses these limitations by infusing semantic knowledge into both the encoding and decoding stages. This integration is facilitated through a twofold mechanism:

1. Supervisory Use in Encoder: Semantic information supervises the encoder to ensure global information is factored into feature representation.
2. Initialization in Decoder: The decoder utilizes semantic cues to initialize and condition the decoding process, enhancing the text prediction capabilities.

Specifically, SEED exemplifies its implementation by integrating the widely recognized ASTER system, leveraging its rectification and recognition pipeline while extending it to incorporate semantic guidance.

Experimental Results

The framework's robustness was evaluated against multiple benchmarks, with noteworthy performances on datasets including ICDAR2015 and SVT-Perspective, which are known for their low-quality imagery. The integration of semantic supervision via the semantic module yielded state-of-the-art results, demonstrating enhanced recognition rates of incomplete characters and improved overall robustness.

Key Findings and Implications

The primary contributions of this research are:

• The introduction of a semantics-enhanced encoder-decoder architecture, effectively integrating global semantic information to refine STR accuracy.
• Empirical evidence demonstrating marked improvements in handling challenging image conditions across several widely-acknowledged benchmarks.
• Practical adaptability, highlighting its potential integration into existing STR systems like ASTER and SAR, thereby broadening the utility of semantic enhancements in digital text recognition workflows.

Future Developments

The paper posits potential future developments including the extension of the semantic framework to an end-to-end text spotting system, integrating detection and recognition tasks under one cohesive semantic umbrella. This foresight suggests a further leap in recognition efficiency and accuracy, particularly beneficial for practical applications in real-world settings where imperfect image conditions are commonplace.

In conclusion, the SEED framework signifies a methodological enhancement to traditional STR paradigms by advocating for the integration of semantic intelligence in the recognition pipeline. Through this approach, the framework addresses key hindrances posed by low-quality images, thereby improving the robustness and accuracy of scene text recognition systems. The proposed integration of semantics into both the encoding process and the decoding strategy provides a compelling argument for further exploration and application in the broader field of computer vision.