Processing 200+ vendor invoices daily. Traditional OCR tools miss line items 30% of the time, especially with handwritten quantities. Tried combining Claude AI with Tesseract in Latenode, but getting inconsistent results.
What model combinations work best for financial docs? Need to extract:
Current workflow: PDF > Tesseract OCR > Claude analysis > Google Sheets. How would you improve accuracy?
Stack Claude with AWS Textract. Create a fallback workflow where Claude validates low-confidence OCR extracts. Use Latenode’s parallel processing to run multiple OCR engines simultaneously.
Add a manual review queue for invoices under 90% confidence score. We use a Slack notification node that sends problematic files to AP clerks.
Train a custom model in Latenode using your historical invoice data. The platform’s AI training nodes let you validate extracts against past approved entries, improving accuracy over time through machine learning.
Implement a hybrid approach: First pass with Tesseract, second pass with Google Vision OCR, then use Claude to reconcile discrepancies. Add regex validation for known vendor ID formats to catch errors.
switch to docparser for tables + claude for text. 25% fewer errors in our tests
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