Deep Unrestricted Document Image Rectification (2304.08796v2)

Abstract: In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://github.com/fh2019ustc/DocTr-Plus.

• The paper presents DocTr++, a hierarchical encoder-decoder model that significantly improves rectification for document images with incomplete boundaries.
• It reformulates pixel-wise mapping and introduces new evaluation metrics and datasets, outperforming state-of-the-art methods on benchmarks.
• Extensive experiments using MSSIM, LD, ED, and CER metrics confirm DocTr++'s robustness and real-world applicability for mobile-captured documents.

Deep Unrestricted Document Image Rectification

The paper "Deep Unrestricted Document Image Rectification" presents an advanced framework, DocTr++, which addresses the limitations of current document image rectification techniques. Emerging from the original DocTr, DocTr++ innovatively offers a solution to rectifying document images that do not require complete document boundaries, thus expanding applicability to unrestricted distorted images encountered frequently in realistic scenarios.

Key Contributions

DocTr++ introduces several noteworthy technical improvements:

1. Hierarchical Encoder-Decoder Architecture: The revised model employs a hierarchical encoder-decoder structure, enhancing multi-scale representation extraction. This approach significantly upgrades the original DocTr architecture, leading to better distortion rectification.
2. Reformulated Mapping Strategy: The authors have reformulated the pixel-wise mapping between distorted and distortion-free document images. This advanced mapping technique is integral in training DocTr++ to handle the unrestricted rectification scenario effectively.
3. Dataset and Metrics Contribution: For comprehensive evaluation, a real-world test set and applicable metrics were introduced. These tools are crucial for assessing rectification quality in unrestricted document images, serving as benchmarks for future research.

Experimental Evaluation

Extensive experiments validate the superiority of DocTr++. On the DocUNet Benchmark and the newly proposed dataset, DocTr++ consistently outperforms existing state-of-the-art methods in terms of both quantitative and qualitative metrics. Key metrics used include MSSIM, LD, ED, and CER, which demonstrate the algorithm's robustness across varied document image types.

The innovative introduction of MSSIM-M and LD-M metrics addresses challenges in evaluating images without complete boundaries, providing a more accurate assessment of image similarity and distortion correction.

Theoretical and Practical Implications

The implications of DocTr++ are expansive:

• Theoretical: The paper pushes the boundary of document image rectification as a field by addressing unrestricted inputs. It challenges existing baseline methods and proposes a robust framework that encompasses a broader range of scenarios.
• Practical: Practically, DocTr++ offers a compelling solution for real-world applications, such as mobile-captured documents, that are often subject to various distortions like partial document exposure or absence of distinct document boundaries. The ability to rectify such images widens the potential for document digitization in diverse use cases, including archival, legal, or educational contexts.

Future Directions

The research opens several avenues for future exploration:

• Integration with Downstream Applications: Future work might explore integration with OCR systems, enhancing the overall efficacy of automatic text recognition pipelines.
• Adapting to Diverse Document Types: While current focus lies on unrestricted documents, exploring the framework’s adaptation to other forms of documents, such as historical manuscripts or multilingual documents, could enhance its utility.
• Exploration of Geometric Constraints: Future research could aim to explicitly leverage geometric and textural attributes during the rectification process, potentially increasing accuracy in more complex document layouts.

In summation, the paper makes significant strides in document image rectification, offering a robust, scalable solution via DocTr++. With both theoretical foundations and pragmatic implementations, it lays a crucial groundwork for future innovations in the field.