Intro: the token classifier is the main contribution; OCR is pluggable

README.md

@@ -11,10 +11,11 @@ Most PII redactors stop at plain text. These models power
 [document-pii-redactor](https://github.com/eka-care/document-pii-redactor),
 which also redacts **document images** and is light enough to deploy on
 **CPU**. They are trained to understand **Indian names, documents, and
-contexts**, and the text model works across **Indian languages**. OCR
-defaults to lightweight **Tesseract**, which keeps memory low and works
-well for PDFs and good-quality images; for more difficult or blurred
-images, **Bring-your-own OCR** lets a model like [**Nemotron OCR**](https://huggingface.co/nvidia/nemotron-ocr-v2) (or
+contexts**, and the text model works across **Indian languages**. The main
+contribution is the **PII token classifier** — OCR is just the pluggable
+input stage in front of it. It defaults to lightweight **Tesseract**, which
+keeps memory low and works well for PDFs and good-quality images; for more
+difficult or blurred images, **Bring-your-own OCR** lets a model like [**Nemotron OCR**](https://huggingface.co/nvidia/nemotron-ocr-v2) (or
 Textract, Google Vision, etc) plug straight in for better results
 ([example notebook](https://github.com/eka-care/document-pii-redactor/blob/main/examples/byo_ocr_nemotron.ipynb)).