Handwritten Text Recognition (HTR) can significantly enhance data capture, but even the most advanced technology may encounter occasional challenges. That’s why manual validation is a crucial step to ensure the highest level of data accuracy.
Here’s how to perform manual validation after HTR:
1) Data Sampling: Select a representative sample of documents from your dataset. This sample should cover various handwriting styles, languages, and document types.
2) Review Process: Assign a team of skilled data validators who are well-versed in your data requirements. They will review the transcribed text for accuracy.
3) Validation Criteria: Establish clear criteria for validation. Define what constitutes an accurate transcription and what should be flagged as an error. Common criteria include spelling, punctuation, and contextual accuracy.
4) Feedback Loop: Create a feedback loop with the HTR system. Document the errors and provide this feedback to improve the HTR model over time. This iterative process enhances accuracy in the long run.
5) Quality Control: Implement quality control measures to ensure consistency in the validation process. Use multiple validators and cross-validate the results.
6) Flagging Errors: Clearly mark errors or discrepancies between the HTR output and the validated text. This helps in identifying areas for model improvement.
7) Feedback to HTR Model: Regularly update and fine-tune the HTR model based on the feedback from the manual validation process. This continuous improvement is essential for accuracy.
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