A Query-based End-to-End Text Spotter with Mixed Supervision for Improved Scene Text Detection and Recognition

Core Concepts

TextFormer, a novel query-based end-to-end text spotter, utilizes a multi-task model design and mixed supervision training to achieve state-of-the-art performance on scene text detection and recognition tasks.

Abstract

The paper proposes TextFormer, a query-based end-to-end text spotter with a Transformer architecture. Key highlights:

TextFormer uses text queries to bridge the classification, segmentation, and recognition branches, allowing for mutual training and optimization.
An Adaptive Global feature aGgregation (AGG) module is introduced to extract features from different orientations for reading arbitrarily-shaped texts, overcoming the limitations of RoI operations.
A mixed supervision training strategy is adopted, utilizing a mixture of weak annotations (text transcriptions only) and full annotations (text regions and transcriptions) to improve the co-optimization of text detection and recognition.
Extensive experiments on various benchmarks demonstrate the superiority of TextFormer, especially on the ambiguous text spotting dataset TDA-ReCTS where it outperforms state-of-the-art methods by 13.2% in 1-NED.

Stats

TextFormer surpasses the state-of-the-art method on TDA-ReCTS dataset by 13.2% in terms of 1-NED.
On ICDAR 2015, TextFormer achieves the best detection and end-to-end performance under "Strong", "Weak", and "General" lexicons.
On Total-Text, TextFormer outperforms the state-of-the-art methods in both "None" and "Full" lexicon settings for end-to-end text spotting.

Quotes

"TextFormer, a novel query-based end-to-end text spotter, utilizes a multi-task model design and mixed supervision training to achieve state-of-the-art performance on scene text detection and recognition tasks."
"An Adaptive Global feature aGgregation (AGG) module is introduced to extract features from different orientations for reading arbitrarily-shaped texts, overcoming the limitations of RoI operations."
"A mixed supervision training strategy is adopted, utilizing a mixture of weak annotations (text transcriptions only) and full annotations (text regions and transcriptions) to improve the co-optimization of text detection and recognition."

Key Insights Distilled From

TextFormer

by Yukun Zhai,X... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2306.03377.pdf

Deeper Inquiries

How can the query-based design of TextFormer be extended to other multi-task computer vision problems beyond text spotting

The query-based design of TextFormer can be extended to other multi-task computer vision problems by adapting the concept of using query embeddings to represent different aspects of the task. For instance, in object detection, instead of using region proposals, object queries can be used to identify potential objects in an image. These object queries can then be used to generate bounding boxes and classify the objects. Similarly, in image segmentation, pixel queries can be utilized to segment different regions of an image. By incorporating query-based modeling, different tasks can be integrated into a unified framework, allowing for joint training and optimization.

What are the potential drawbacks or limitations of the mixed supervision training approach, and how can they be addressed

One potential drawback of mixed supervision training is the challenge of balancing the influence of weak annotations and full labels on the model's learning process. Weak annotations may not provide sufficient information for the model to learn complex patterns effectively, leading to suboptimal performance. To address this limitation, strategies such as curriculum learning, where the model is gradually exposed to more challenging tasks, can be implemented. Additionally, incorporating self-supervised learning techniques can help the model learn from unlabeled data, reducing the reliance on annotated data.

What other global feature extraction techniques could be explored to further improve the recognition of arbitrarily-shaped texts

In addition to the AGG module used in TextFormer, other global feature extraction techniques could be explored to further improve the recognition of arbitrarily-shaped texts. One approach could be to incorporate graph-based methods to capture long-range dependencies in the text layout. Graph neural networks can model the relationships between different text elements and extract features based on the graph structure. Another technique could involve attention mechanisms that dynamically focus on relevant parts of the text instance, allowing the model to adaptively extract global features based on the context of the text. By exploring these alternative global feature extraction techniques, the model's ability to recognize arbitrarily-shaped texts can be enhanced.