The automatic recognition of tabular data in document images presents a
significant challenge due to the diverse range of table styles and complex
structures. Tables offer valuable content representation, enhancing the
predictive capabilities of various systems such as search engines and Knowledge
Graphs. Addressing the two main problems, namely table detection (TD) and table
structure recognition (TSR), has traditionally been approached independently.
In this research, we propose an end-to-end pipeline that integrates deep
learning models, including DETR, CascadeTabNet, and PP OCR v2, to achieve
comprehensive image-based table recognition. This integrated approach
effectively handles diverse table styles, complex structures, and image
distortions, resulting in improved accuracy and efficiency compared to existing
methods like Table Transformers. Our system achieves simultaneous table
detection (TD), table structure recognition (TSR), and table content
recognition (TCR), preserving table structures and accurately extracting
tabular data from document images. The integration of multiple models addresses
the intricacies of table recognition, making our approach a promising solution
for image-based table understanding, data extraction, and information retrieval
applications. Our proposed approach achieves an IOU of 0.96 and an OCR Accuracy
of 78%, showcasing a remarkable improvement of approximately 25% in the OCR
Accuracy compared to the previous Table Transformer approach.

我们提出了一种端到端的流程，通过集成深度学习模型实现图像中的表格自动识别，解决了表格检测、表格结构识别和表格内容识别的问题，提高了准确性和效率。

TC-OCR：高效检测与识别表格结构和内容的 TableCraft OCR

TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table  Structure & Content

We present TableBank, a new image-based table detection and recognition
dataset built with novel weak supervision from Word and Latex documents on the
internet. Existing research for image-based table detection and recognition
usually fine-tunes pre-trained models on out-of-domain data with a few thousand
human-labeled examples, which is difficult to generalize on real-world
applications. With TableBank that contains 417K high quality labeled tables, we
build several strong baselines using state-of-the-art models with deep neural
networks. We make TableBank publicly available and hope it will empower more
deep learning approaches in the table detection and recognition task. The
dataset and models are available at
https://github.com/doc-analysis/TableBank.

TableBank 使用 Word 和 Latex 文档的弱监督，构建了一个新的基于图像的表格检测和识别数据集。该数据集包含 417K 个高质量标记的表格，并利用深度神经网络构建了多个强基线模型，旨在为表格检测和识别任务提供更多的深度学习方法。