- The paper presents a comprehensive survey categorizing deep learning approaches for VRDU tasks such as Key Information Extraction, Question Answering, and Entity Linking.
- It reviews both mono-task and multi-task frameworks, detailing architectures like transformers and graph-based models for document content understanding.
- The survey highlights emerging trends, including LLM integration and challenges in processing long, complex, multimodal documents.
An Expert Review of "Deep Learning based Visually Rich Document Content Understanding: A Survey"
The paper "Deep Learning-based Visually Rich Document Content Understanding: A Survey" authored by Yihao Ding, Jean Lee, and Soyeon Caren Han, offers a comprehensive review of frameworks and methodologies in the field of Visually Rich Document Understanding (VRDU). Through a meticulous examination of recent advancements, this work presents a taxonomical survey, categorizing and analyzing various approaches, benchmark datasets, pretraining methodologies, model architectures, and technical implementation details pertinent to VRDU tasks. The intended audience for this review consists of researchers and practitioners with a vested interest in deep learning applications for document understanding.
Overview of VRDU Tasks
Visually Rich Documents (VRDs), ubiquitous in domains such as finance, medicine, and academia, amalgamate textual, visual, and layout-based information, necessitating advanced techniques for effective information extraction. The paper delineates key VRDU tasks into three primary categories: Key Information Extraction (KIE), Question Answering (QA), and Entity Linking (EL). These tasks leverage multimodal information to enhance the comprehension and extraction of pertinent details from VRDs.
Mono-Task VRDU Frameworks
Key Information Extraction
The paper organizes KIE models into five main categories: Feature-driven models, Joint-learning frameworks, Relation-aware models, Few/Zero-shot learning frameworks, and Prompt-learning frameworks. Early works like Chargrid and ACP integrated visual and textual features to generate rich contextual embeddings, while more recent models such as LayoutLM and its variants employed bidirectional transformers to leverage pretraining tasks focused on layout-aware features.
Models like PICK and FormNet adopted graph-based approaches to capture spatial and logical relations between document entities, enhancing the representation of context-aware embeddings. Frameworks designed for few/zero-shot scenarios, including LASER and QueryForm, were developed to address the scarcity of annotated data through meta-learning and prompting mechanisms. Notably, the rise of LLMs and MLLMs has encouraged models like GenKIE and ICL-D3IE to utilize structured prompts for adaptive and generalized information extraction.
Entity Linking
Entity linking frameworks harness both entity-level and token-level approaches to establish semantic correlations between document elements. DocStruct and KVPFormer exemplify entity-level approaches that formulate entity linking as a dependency parsing or question-answering problem. Token-level approaches, such as those by Carbonell et al., employ graph-based strategies to capture relational information at a more granular level, facilitating the identification of logical hierarchies within documents.
Visual Question Answering
Given the increasing complexity of documents, frameworks for Visual Question Answering (VQA) have evolved from single-page models such as those benchmarked on DocVQA to sophisticated multi-page models. Hi-VT5 and GRAM leverage hierarchical architectures and global attention mechanisms, respectively, to manage the intricacies of multi-page documents, expanding beyond the limitations of traditional models by enabling efficient information retrieval and contextual understanding across numerous pages.
Multi-Task VRDU Frameworks
Fine-Grained Pretrained Models
The integration of textual, visual, and layout features has significantly advanced the landscape of VRDU. Models like LayoutLMv2 and LayoutLMv3 epitomize visual-integrated approaches that employ transformers and sophisticated pretraining tasks like Masked Visual-LLMing (MVLM) to achieve state-of-the-art performance in various VRDU tasks. These encoder-only models excel at capturing fine-grained details, although they are often constrained by fixed input lengths, impeding their applicability to long documents.
Coarse and Joint-Grained Pretrained Models
To address the limitations of fine-grained models, coarse-grained and joint-grained frameworks have been developed. Models such as SelfDoc and UniDoc use entity-level information to condense document elements, improving computational efficiency while retaining essential contextual details. Joint-grained models like StrucText and MGDoc further amalgamate fine and coarse-grained features, bridging the gap between granularity levels to produce robust document representations.
Encoder-Decoder Pretrained Frameworks
Encoder-decoder frameworks like TILT and Donut predominantly utilize generative approaches, facilitating tasks such as VQA and KIE through end-to-end processing of VRDs. These frameworks, however, often require extensive OCR preprocessing, which can accumulate errors. OCR-free models like ReRum mitigate this by directly interpreting document images, although they may necessitate additional computational resources to achieve comparable performance to OCR-dependent counterparts.
Emerging Trends and Future Directions
The paper highlights several challenges and avenues for future research. Despite significant advancements, the field still grapples with tasks involving long-term dependencies and zero-shot learning. Emerging areas of interest include the application of LLMs and MLLMs to VRDU, with models like LayoutLLM and HRVDA pioneering the integration of high-resolution images and layout-aware pretraining to enhance document understanding capabilities.
Conclusion
Ding et al.'s survey offers an exhaustive and insightful examination of the current state of VRDU, emphasizing both the strides made and the hurdles that remain. The research underscores the importance of multimodal integration, sophisticated pretraining tasks, and innovative model architectures in driving the field forward. It serves as a valuable reference for researchers and practitioners aiming to develop or refine VRDU systems, fostering informed approaches to leverage deep learning for comprehensive document understanding. The survey sets the stage for continued exploration and optimization, fostering advancements that promise to bridge existing gaps and meet future demands in visually rich document content understanding.