Leveraging Existing Table Structures and Content to Synthesize Realistic Data for Improving Table Recognition

المفاهيم الأساسية

A novel method for synthesizing high-quality, realistic table annotation data by leveraging the structure and content of existing complex tables, enabling the efficient creation of tables that closely replicate authentic styles found in the target domain.

الملخص

The paper proposes a novel method for synthesizing annotation data specifically designed for table recognition. This method utilizes the structure and content of existing complex tables, facilitating the efficient creation of tables that closely replicate the authentic styles found in the target domain.
Key highlights:

Existing large-scale, real-world table annotation datasets are limited in their applicability across different domains and languages due to similarities in table styles. Automatically annotated datasets also frequently contain a significant number of errors.
To address these limitations, the authors propose a method that utilizes the structure and content of existing complex tables to generate high-quality, realistic synthetic datasets tailored to the target application domain.
The method involves analyzing the distribution of real tables in the target domain, extracting style profiles, and then transforming the source tables into new target tables with different yet realistic styles.
The authors synthesized the first large-scale table annotation dataset in the domain of Chinese financial announcements and established the inaugural benchmark for real-world complex tables in this domain.
The method was also used to augment the FinTabNet dataset, extracted from English financial announcements, by increasing the proportion of more complex tables with multiple spanning cells. Experiments show that models trained on this augmented dataset achieve comprehensive improvements in performance, especially in recognizing more complex tables.

الإحصائيات

Approximately 9% of the tables in the FinTabNet dataset had obvious annotation errors.
The authors sampled 105,600 bordered tables from nearly 1.5 million tables extracted from Chinese financial announcements as the data source for table synthesis.
The authors sampled 2,290 real tables from Chinese financial announcements, including 1,000 bordered tables and 1,290 borderless tables, to create the benchmark for complex tables in the Chinese financial announcement domain.

اقتباسات

"To create large-scale datasets for table recognition, some researchers have initially started by utilizing specific repositories of scientific papers or financial reports. Each document in these repositories contains tables that correspond to some form of structured source codes (such as LaTeX, XML, HTML)."
"To adapt more efficiently to a wider range of application domains, some researchers are exploring methods for synthesizing annotated data for table recognition. These methods largely depend on predefined structural templates and randomly selected text to generate table structures and fill in content."
"Inspired by this observation, we propose synthesizing new target tables by leveraging the structure and content of existing complex tables, while applying completely different yet realistically plausible styles to these target tables. This approach ensures that the synthesized complex tables more closely resemble real-world scenarios than those produced by methods relying on randomly generated structures and content."

الرؤى الأساسية المستخلصة من

Synthesizing Realistic Data for Table Recognition

by Qiyu Hou,Jun... في arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11100.pdf

استفسارات أعمق

How can the proposed table synthesis method be extended to other domains beyond finance, such as healthcare or legal documents, to further improve the diversity and applicability of the synthesized data

The proposed table synthesis method can be extended to other domains beyond finance by adapting the approach to the specific characteristics and requirements of each domain. For example, in healthcare, where tables often contain patient data, medical records, and treatment plans, the synthesis method can be tailored to incorporate medical terminology, patient identifiers, and healthcare-specific formatting. By analyzing a large dataset of medical documents, similar to the financial announcements in the original study, researchers can extract the structure and content of tables to create a diverse set of synthetic tables. Additionally, incorporating domain-specific attributes such as medical codes, treatment categories, and healthcare provider information can enhance the realism and relevance of the synthesized data for healthcare applications.
In legal documents, tables are commonly used for presenting case details, legal precedents, and contract terms. The synthesis method can be adapted to capture the unique formatting requirements of legal tables, including legal citations, case numbers, and legal terminology. By analyzing a corpus of legal documents, researchers can extract the structure and content of tables to generate synthetic data that closely resembles real legal tables. Incorporating attributes such as legal section headings, case references, and legal entity names can improve the authenticity and utility of the synthesized data for legal document analysis.
Overall, by customizing the table synthesis method to the specific characteristics and requirements of different domains, researchers can create diverse and realistic synthetic data sets that cater to a wide range of applications beyond finance.

What are the potential limitations or drawbacks of the current table synthesis approach, and how could it be further improved to address more complex table structures or styles

One potential limitation of the current table synthesis approach is the complexity of replicating highly intricate or unique table structures and styles found in real-world documents. While the method leverages existing tables to generate new synthetic data, it may struggle to accurately capture the nuances of extremely complex tables with unconventional layouts or formatting. To address this limitation and improve the synthesis approach, several enhancements can be considered:

Enhanced Style Variation: Introduce a wider range of style profiles and attributes to better simulate diverse table styles found in different domains. This can include incorporating more detailed font types, alignment options, and border styles to capture the complexity of real-world tables.

Advanced Text Generation: Implement advanced text generation techniques to generate more realistic and contextually relevant text content within cells. This can involve utilizing natural language processing models to generate text that aligns with the semantics of the table.

Dynamic Profile Selection: Develop a mechanism to dynamically adjust style profiles based on the complexity of the source table, allowing for more accurate synthesis of complex table structures.

Iterative Refinement: Implement an iterative refinement process where synthesized tables are evaluated and refined based on feedback from domain experts to enhance the realism and accuracy of the generated data.

By incorporating these enhancements, the table synthesis approach can overcome limitations and produce synthetic data that more closely resembles the complexity and diversity of real-world tables.

Given the importance of table recognition in various applications, how could the insights and techniques from this work be leveraged to develop more robust and generalizable table recognition models that can handle a wider range of table structures and styles across different domains

The insights and techniques from this work can be leveraged to develop more robust and generalizable table recognition models by focusing on the following strategies:

Transfer Learning: Utilize transfer learning techniques to adapt pre-trained models on synthesized data from one domain to improve performance on new domains. By fine-tuning models on domain-specific data, the models can learn to recognize a wider range of table structures and styles.

Multi-Domain Training: Train models on a diverse dataset that includes tables from various domains to enhance the model's ability to generalize across different types of tables. By exposing the model to a broad spectrum of table structures and styles, it can learn to adapt to new domains more effectively.

Ensemble Learning: Implement ensemble learning techniques to combine the predictions of multiple table recognition models trained on different datasets. By aggregating the outputs of diverse models, the ensemble can achieve higher accuracy and robustness in recognizing tables across domains.

Continuous Evaluation and Improvement: Establish a feedback loop where the performance of table recognition models is continuously evaluated on real-world data, including tables from different domains. This feedback can be used to refine the models, update training data, and enhance the model's ability to handle a wider range of table structures and styles.

By incorporating these strategies, researchers can develop more versatile and adaptable table recognition models that can effectively handle the complexities of tables across diverse domains.