betekintés - Computer Vision - # Image-based Table Recognition

Efficient Detection and Recognition of Table Structure and Content Using Deep Learning Models

Q: How can the proposed pipeline be extended to handle more complex table structures, such as merged cells, nested tables, or irregular layouts?

To handle more complex table structures like merged cells, nested tables, or irregular layouts, the proposed pipeline can be extended in the following ways: Advanced Table Structure Recognition Models: Integrate more sophisticated table structure recognition models that can identify and differentiate merged cells, nested tables, and irregular layouts. These models should be able to understand the hierarchical relationships between different elements within the table. Enhanced Text Detection and Recognition: Improve the text detection and recognition capabilities to accurately identify text within merged cells or nested tables. This may involve training the OCR model on a more diverse dataset that includes complex table structures. Customized Data Mapping Algorithms: Develop customized algorithms for mapping text to specific cells in cases of merged cells or nested tables. These algorithms should be able to handle overlapping text regions and assign them to the correct cell. Visual Element Recognition: Incorporate models for recognizing visual elements like images or graphs within tables. This will enhance the pipeline's ability to process multimodal tables effectively. Iterative Refinement: Implement an iterative refinement process where the pipeline can revisit and refine its initial predictions based on contextual information and feedback loops. This can help in resolving ambiguities in complex table structures. By incorporating these enhancements, the pipeline can be better equipped to handle the intricacies of complex table structures, ensuring accurate extraction and understanding of data from diverse table layouts.

Q: How can the integration of the three models (DETR, CascadeTabNet, and PP OCR v2) be further optimized to improve the overall efficiency and performance of the table recognition system?

To optimize the integration of the three models (DETR, CascadeTabNet, and PP OCR v2) for improved efficiency and performance in table recognition, the following strategies can be implemented: Model Fusion Techniques: Explore advanced model fusion techniques to seamlessly integrate the outputs of DETR, CascadeTabNet, and PP OCR v2. This can involve ensemble methods, where the strengths of each model are combined to enhance overall performance. Joint Training: Consider joint training of the models to improve coordination and coherence between different components of the pipeline. Fine-tuning the models together on a unified objective can lead to better synergy and optimized performance. Optimized Hyperparameters: Conduct thorough hyperparameter tuning to find the optimal configuration for each model and the overall pipeline. Fine-tuning parameters such as learning rates, batch sizes, and model architectures can significantly impact performance. Data Augmentation: Implement data augmentation techniques to increase the diversity and volume of training data. Augmenting the dataset with variations of table structures and content can improve the models' generalization capabilities. Incremental Learning: Explore incremental learning strategies to continuously update and refine the models based on new data and feedback. This adaptive learning approach can help the system adapt to evolving table structures and content patterns. By implementing these optimization strategies, the integration of DETR, CascadeTabNet, and PP OCR v2 can be fine-tuned to achieve higher efficiency, accuracy, and robustness in table recognition tasks.

Alapfogalmak

An integrated pipeline combining DETR, CascadeTabNet, and PP OCR v2 models achieves simultaneous and accurate table detection, structure recognition, and content extraction from document images.

Kivonat

The researchers propose a comprehensive pipeline that integrates three distinct deep learning models - DETR for table detection, CascadeTabNet for table structure recognition, and PP OCR v2 for text detection and recognition. This integrated approach effectively handles diverse table styles, complex structures, and image distortions commonly encountered in document images.

The key highlights of the methodology are:

DETR, a transformer-based object detection model, is used to accurately localize tables within the input document.
CascadeTabNet, an advanced end-to-end deep learning framework, performs pixel-level table segmentation and cell segmentation, enabling precise extraction of the table's structural information.
PP OCR v2 is employed for accurate text detection and recognition within the identified table cells, with a flexible mapping process to align the text with the corresponding table cells.

The integrated pipeline demonstrates superior performance compared to existing methods like Table Transformer. It achieves an IOU of 0.96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in OCR Accuracy.

The proposed approach contributes to the advancement of image-based table recognition techniques, offering a promising solution for handling diverse table layouts in real-world scenarios and enhancing data extraction and comprehension in digitized documents.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The proposed model achieves an IOU of 0.96 and an OCR Accuracy of 78%, which is a remarkable improvement of approximately 25% in OCR Accuracy compared to the previous Table Transformer approach.

Idézetek

"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."

Főbb Kivonatok

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

by Avinash Anan... : arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10305.pdf

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

Mélyebb kérdések

How can the proposed pipeline be extended to handle more complex table structures, such as merged cells, nested tables, or irregular layouts?

To handle more complex table structures like merged cells, nested tables, or irregular layouts, the proposed pipeline can be extended in the following ways:

Advanced Table Structure Recognition Models: Integrate more sophisticated table structure recognition models that can identify and differentiate merged cells, nested tables, and irregular layouts. These models should be able to understand the hierarchical relationships between different elements within the table.

Enhanced Text Detection and Recognition: Improve the text detection and recognition capabilities to accurately identify text within merged cells or nested tables. This may involve training the OCR model on a more diverse dataset that includes complex table structures.

Customized Data Mapping Algorithms: Develop customized algorithms for mapping text to specific cells in cases of merged cells or nested tables. These algorithms should be able to handle overlapping text regions and assign them to the correct cell.

Visual Element Recognition: Incorporate models for recognizing visual elements like images or graphs within tables. This will enhance the pipeline's ability to process multimodal tables effectively.

Iterative Refinement: Implement an iterative refinement process where the pipeline can revisit and refine its initial predictions based on contextual information and feedback loops. This can help in resolving ambiguities in complex table structures.

By incorporating these enhancements, the pipeline can be better equipped to handle the intricacies of complex table structures, ensuring accurate extraction and understanding of data from diverse table layouts.

How can the integration of the three models (DETR, CascadeTabNet, and PP OCR v2) be further optimized to improve the overall efficiency and performance of the table recognition system?

To optimize the integration of the three models (DETR, CascadeTabNet, and PP OCR v2) for improved efficiency and performance in table recognition, the following strategies can be implemented:

Model Fusion Techniques: Explore advanced model fusion techniques to seamlessly integrate the outputs of DETR, CascadeTabNet, and PP OCR v2. This can involve ensemble methods, where the strengths of each model are combined to enhance overall performance.

Joint Training: Consider joint training of the models to improve coordination and coherence between different components of the pipeline. Fine-tuning the models together on a unified objective can lead to better synergy and optimized performance.

Optimized Hyperparameters: Conduct thorough hyperparameter tuning to find the optimal configuration for each model and the overall pipeline. Fine-tuning parameters such as learning rates, batch sizes, and model architectures can significantly impact performance.

Data Augmentation: Implement data augmentation techniques to increase the diversity and volume of training data. Augmenting the dataset with variations of table structures and content can improve the models' generalization capabilities.

Incremental Learning: Explore incremental learning strategies to continuously update and refine the models based on new data and feedback. This adaptive learning approach can help the system adapt to evolving table structures and content patterns.

By implementing these optimization strategies, the integration of DETR, CascadeTabNet, and PP OCR v2 can be fine-tuned to achieve higher efficiency, accuracy, and robustness in table recognition tasks.