Best Practice

Configuring document types in Docbits requires care and expertise to ensure that document processing is efficient and accurate. Here are some best practices for configuring document types, including recommendations for setting up effective regex patterns and tips for training models to improve accuracy:

Planning and analysis

Best practices

Requirements analysis:

• Conduct a thorough analysis of the requirements to understand which document types are needed and what information needs to be extracted from them.

Pilot projects:

• Start with pilot projects to test the configuration and extraction rules before applying them to the entire system.

Setting up layouts

Best practices

Consistency:

• Make sure that documents of one type have a consistent layout. This makes configuration and data extraction easier.

Use templates:

• Use document templates to ensure consistency and simplify setup.

Field definitions and metadata

Best practices

Unique field names:

• Use unique and meaningful names for fields to avoid confusion.

Relevant metadata:

• Define only the fields that are really necessary to reduce complexity and increase efficiency.

Formatting guidelines:

• Set clear formatting guidelines for each field to facilitate validation and extraction

Training models for data extraction

Best practices

Use quality data:

• Use high-quality and representative data to train the models.

Data enrichment:

• Enrich the training dataset by adding different document examples to increase the robustness of the model.

Iterative training:

• Train the model iteratively and evaluate the results regularly to achieve continuous improvements.

Tips:

Transfer learning:

• Leverage pre-trained models and tune them with specific document examples to reduce training time and increase accuracy.

Hyperparameter tuning:

• Experiment with different hyperparameters to find the optimal configuration for your model.

Validation and extraction rules

Best practices

Multi-step validation:

• Implement multi-step validation rules to check the correctness of the extracted data.

Combine rule-based and ML-based approaches:

• Use a combination of rule-based and machine learning approaches to extract and validate data.

Error management:

• Set up mechanisms to detect and fix faulty extractions.

Automation workflows

Best practices

Clearly defined workflows:

• Define clear and traceable automation workflows for each document type.

Continuous monitoring:

• Monitor automation workflows regularly to evaluate their performance and identify optimization potential.

Incorporate user feedback:

• Integrate user feedback to continuously improve workflows.

User rights and access control

Best practices

Role-based access:

• Implement role-based access controls to ensure that only authorized users have access to certain document types and fields.

Regular review:

• Regularly review access controls and adapt them to changing requirements.

Configuring document types in Docbits requires careful planning and continuous adjustment to achieve optimal results. By applying the best practices above, you can significantly increase the efficiency and accuracy of document processing and data extraction.