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Azure AI Document Intelligence Read Optical Character Recognition (OCR) model runs at a higher resolution than Azure AI Vision Read and extracts print and handwritten text from PDF documents and scanned images.
The Read OCR model / document layout model extracts print and handwritten style text as lines
and words
. This feature applies to supported handwritten languages. Please check if your concerned language is under the supported list.
To extract printed and handwritten text along with barcodes, formulas and font styles from images and documents:
Read model DI studio link: https://documentintelligence.ai.azure.com/studio/read
Layout model DI studio link: https://documentintelligence.ai.azure.com/studio/layout
If you have already tried the above and feel that the identification and accuracy needs to be improved then follow the below:
Action Plan: It is possible to improve the accuracy of handwritten text extraction using Azure AI Document Intelligence. Here are some strategies you can consider:
- Custom Models: Custom models generate an estimated accuracy score when trained. Documents analyzed with a custom model produce a confidence score for extracted fields. You can use these scores to interpret the accuracy and improve the results.
- Confidence Scores: Document Intelligence analysis results return an estimated confidence for predicted words, key-value pairs, selection marks, regions, and signatures. You can use these confidence scores to determine whether to automatically accept the prediction or flag it for human review.
- Training Data: Ensure that all variations of a document are included in the training dataset. This can help produce a model with higher accuracy and confidence scores during analysis and reduce the number of documents flagged for human review.
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