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Deciding whether to train multiple template models or a single model to handle variations in tax deduction forms is a common challenge in document processing, especially when dealing with highly variable formats. Here are some considerations to help you decide the best approach for your use case:
- Variability of Forms: Multiple Models: If the variations in forms are significantly different in layout and structure, training separate models for each type of form might yield higher accuracy. This approach is beneficial when the documents can be easily categorized into distinct types. Single Model: If the variations are more about the presence or absence of certain fields rather than entirely different layouts, a single, more robust model might be sufficient.
- Post-Processing Efforts: Training multiple models could potentially reduce the amount of post-processing needed, as each model would be tailored to a specific form type. However, this comes at the cost of increased complexity in maintaining and updating multiple models. A single model might require more sophisticated post-processing logic to handle the variations, but it simplifies the model management aspect.
- Training Data Availability: More models mean you need enough representative data for each type of form. Ensure you have sufficient and diverse data samples for each category. A single model requires diverse data that covers all variations and combinations seen in the forms.
- Accuracy and Manual Review: If accuracy is crucial, you might lean towards multiple models, as this could provide more precise results for each form type. Implement a confidence scoring mechanism. Documents that the model processes with low confidence can be flagged for manual review.
- Scalability and Maintenance: Multiple models can be challenging to scale and maintain, especially if new form types are introduced or existing forms are updated. A single model is easier to update and scale, but it must be robust enough to handle all variations.
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