  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.
