IDP Auto extraction Accuracy

Certified Senior Developer

i am using idp to extract data from documents like passport, DL etc, after several reconciliation also auto extraction accuracy is not increasing ,can you please help me with this?

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    Certified Associate Developer

    Here are few points which can be helpful.

    1. Check the quality of your source documents: Ensure that the documents you're using are high-quality, legible, and have no missing information.

    2. Train your system with more data: IDP systems use machine learning algorithms to recognize patterns in data. The more data you feed into your system, the better it will become at recognizing patterns and making accurate predictions.

    3. Refine your IDP models: Your IDP models may need to be refined to improve their accuracy. For example, you may need to adjust your model's rules to better recognize specific types of data or adjust the weighting of different features to prioritize certain types of data.

    4. Incorporate human review: Although IDP technology can significantly reduce the need for manual data entry, it's still important to incorporate a human review process to catch any errors that the system may have missed. You can use the output from your IDP system to highlight potential errors, which can then be reviewed by a human reviewer.

    5. Explore other IDP solutions: There are many IDP solutions available on the market, each with its own strengths and weaknesses. It's possible that a different IDP solution may be better suited to your specific use case.

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  • 0
    Certified Associate Developer

    Here are few points which can be helpful.

    1. Check the quality of your source documents: Ensure that the documents you're using are high-quality, legible, and have no missing information.

    2. Train your system with more data: IDP systems use machine learning algorithms to recognize patterns in data. The more data you feed into your system, the better it will become at recognizing patterns and making accurate predictions.

    3. Refine your IDP models: Your IDP models may need to be refined to improve their accuracy. For example, you may need to adjust your model's rules to better recognize specific types of data or adjust the weighting of different features to prioritize certain types of data.

    4. Incorporate human review: Although IDP technology can significantly reduce the need for manual data entry, it's still important to incorporate a human review process to catch any errors that the system may have missed. You can use the output from your IDP system to highlight potential errors, which can then be reviewed by a human reviewer.

    5. Explore other IDP solutions: There are many IDP solutions available on the market, each with its own strengths and weaknesses. It's possible that a different IDP solution may be better suited to your specific use case.

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