Table Cell Detector : A Revolutionizing Table Cell Detection System in Documents Using LCNN and PyTorch

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By Next SolutionLab on 2024-10-22 20:53:11

Introduction

In today’s digital age, extracting data from documents efficiently is critical for businesses, especially when dealing with structured information like tables. Standard OCR systems often fail to capture the exact structure of tables, leading to inaccuracies in data extraction. To address this issue, We developed an advanced Table Cell Detection System that detects vertical and horizontal lines and identifies table cells with remarkable precision. This system uses LCNN (Line Connectivity Neural Network) for line detection and a unique post-processing approach for accurate cell extraction.

Key Features

1. Line Detection with LCNN:

At the core of this system is the LCNN model, a specialized neural network for detecting vertical and horizontal lines in table images. Trained on 70,000 annotated data, the model achieves an impressive 98.63% accuracy. It effectively identifies lines in complex table structures and varying document formats, ensuring that no lines are missed.

To know more about LCNN, please go through this paper.

2. Accurate Table Cell Detection:

After line detection, the system generates a mask from the detected lines. A post-processing step then updates any partial lines and calculates the join points of vertical and horizontal lines. Using this information, the system detects merged cells by identifying row and column merges, achieving 98.89% accuracy for cell detection. This ensures that even complex, multi-cell tables are accurately reconstructed.

3. Independent Modules:

The system is built with two independent modules:

LineDetector: Detects the lines in the table image.

CellExtraction: Finds table cells based on the detected lines and determines merge patterns.

These modules can be used independently or together in an end-to-end process, making the system highly flexible and customizable.

How It Works

The process begins with an input of either a table image or a full document image with table coordinates. The LineDetector module identifies vertical and horizontal lines using LCNN and generates a mask. The CellExtraction module processes this mask to find the join points and identify merged cells by analyzing row and column merges. The final output is a JSON file that contains the positions of the detected cells.

                                                                                                     Input image                                                                                                       Output Line

                               

                                                 Output Cell                                                                                                    Output Cell

Applications

OCR Enhancement: Helps OCR systems accurately reconstruct table structures in a digital format.

Data Extraction: Ideal for extracting structured data from invoices, forms, reports, and other documents.

Document Digitization: Converts tables in physical documents to digital formats for easy processing and analysis.

Why It Stands Out

Accurate table cell detection is crucial for maintaining the structure of data during the digitization process. Many OCR systems struggle to replicate tables exactly as they appear in documents, leading to data loss or inaccuracies. By using LCNN and advanced post-processing, this system bridges the gap between physical and digital table representations, making it easier for businesses to automate data entry and analysis tasks with confidence.

Conclusion

This Table Cell Detection System offers a highly accurate and efficient solution to one of the most common challenges in document processing: replicating tables as they appear in the original document. With its modular design and use of cutting-edge technologies, it can easily be integrated into existing workflows or used as a standalone solution for complex table detection tasks.