[Bug]: Low Quality In Parsing Business License Of China

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Introduction

As a user of RAGFlow, we have encountered an issue with the parsing of business licenses from China. Despite the well-formatted nature of these documents, the output quality is low. In this report, we will outline the steps to reproduce the issue, provide additional information, and discuss the expected behavior.

Self Checks

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  • We have searched for existing issues, including closed ones, to ensure that this issue has not been reported before. Search for existing issues
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  • We understand that non-English title submissions will be closed directly, as per the language policy. Language Policy
  • We will not modify this template and fill in all the required fields.

RAGFlow Workspace Code Commit ID

haha

RAGFlow Image Version

0.18.2

Other Environment Information

// Add environment information here

Actual Behavior

When uploading a PDF document containing a China business license, the output quality is low. This issue could be avoided if the business license was not well-formatted. However, as mentioned, China business licenses are typically well-formatted.

Expected Behavior

No response

Steps to Reproduce

// Add steps to reproduce the issue here

Additional Information

No response

Analysis and Solution

To resolve this issue, we need to analyze the parsing algorithm used by RAGFlow to identify the root cause of the low-quality output. We can then modify the algorithm to improve the output quality for China business licenses.

Possible Causes

  1. Insufficient Training Data: The parsing algorithm may not have been trained on a sufficient number of China business licenses, leading to low-quality output.
  2. Inadequate Preprocessing: The preprocessing step may not be removing unnecessary information from the document, leading to low-quality output.
  3. Inaccurate OCR: The OCR (Optical Character Recognition) step may not be accurately recognizing the text in the document, leading to low-quality output.

Solution

To resolve this issue, we can take the following steps:

  1. Collect More Training Data: Collect a larger dataset of China business licenses to train the parsing algorithm.
  2. Improve Preprocessing: Modify the preprocessing step to remove unnecessary information from the document.
  3. Improve OCR: Improve the OCR step to accurately recognize the text in the document.

Conclusion

In conclusion, the low-quality output of China business licenses in RAGFlow is a significant issue that needs to be addressed. By analyzing the parsing algorithm and identifying the root cause of the issue, we can modify the algorithm to improve the output quality. We can collect more training data, improve preprocessing, and improve OCR to resolve this issue.

Recommendations

Based on our analysis, we recommend the following:

  1. Collect More Training Data: Collect a larger dataset of China business licenses to train the parsing algorithm.
  2. Improve Preprocessing: Modify the preprocessing step to remove unnecessary information from the document.
  3. Improve OCR: Improve the OCR step to accurately recognize the text in the document.

Frequently Asked Questions

Q: What is the issue with parsing business licenses from China in RAGFlow?

A: The issue is that the output quality is low, despite the well-formatted nature of these documents.

Q: Why is the output quality low?

A: The output quality is low because the parsing algorithm used by RAGFlow may not have been trained on a sufficient number of China business licenses, leading to inaccurate recognition of the text in the document.

Q: What are the possible causes of the low-quality output?

A: The possible causes of the low-quality output are:

  1. Insufficient Training Data: The parsing algorithm may not have been trained on a sufficient number of China business licenses.
  2. Inadequate Preprocessing: The preprocessing step may not be removing unnecessary information from the document.
  3. Inaccurate OCR: The OCR step may not be accurately recognizing the text in the document.

Q: How can the issue be resolved?

A: The issue can be resolved by:

  1. Collecting More Training Data: Collecting a larger dataset of China business licenses to train the parsing algorithm.
  2. Improving Preprocessing: Modifying the preprocessing step to remove unnecessary information from the document.
  3. Improving OCR: Improving the OCR step to accurately recognize the text in the document.

Q: What are the benefits of resolving this issue?

A: The benefits of resolving this issue are:

  1. Improved Output Quality: The output quality of China business licenses will be improved, providing a better user experience for our customers.
  2. Increased Accuracy: The accuracy of the parsing algorithm will be increased, reducing the likelihood of errors.
  3. Enhanced Customer Satisfaction: Our customers will be satisfied with the improved output quality and accuracy of the parsing algorithm.

Q: How can I provide feedback on this issue?

A: You can provide feedback on this issue by:

  1. Commenting on this article: You can comment on this article to provide feedback and suggestions.
  2. Reporting the issue: You can report the issue on the RAGFlow issue tracker.
  3. Contacting the support team: You can contact the support team to provide feedback and suggestions.

Q: What is the next step in resolving this issue?

A: The next step in resolving this issue is to:

  1. Collect more training data: Collect a larger dataset of China business licenses to train the parsing algorithm.
  2. Improve preprocessing: Modify the preprocessing step to remove unnecessary information from the document.
  3. Improve OCR: Improve the OCR step to accurately recognize the text in the document.

Conclusion

In conclusion, the low-quality output of China business licenses in RAGFlow is a significant issue that needs to be addressed. By analyzing the parsing algorithm and identifying the root cause of the issue, we can modify the algorithm to improve the output quality. We can collect more training data, improve preprocessing, and improve OCR to resolve this issue.

Recommendations

Based on our analysis, we recommend the following:

  1. Collect More Training Data: Collect a larger dataset of China licenses to train the parsing algorithm.
  2. Improve Preprocessing: Modify the preprocessing step to remove unnecessary information from the document.
  3. Improve OCR: Improve the OCR step to accurately recognize the text in the document.

By following these recommendations, we can improve the output quality of China business licenses in RAGFlow and provide a better user experience for our customers.