Update PyTesseract.qmd

Author: qualiaMachine

Toolbox/Libraries/PyTesseract.qmd

@@ -20,9 +20,9 @@ categories:
 
 ## About this resource
 
-[Pytesseract](https://pypi.org/project/pytesseract/) is a Python wrapper for [Google’s Tesseract OCR engine](https://github.com/tesseract-ocr/tesseract), used for recognizing and extracting text from images. It works on a wide range of image types (e.g., JPEG, PNG, TIFF) and supports over 100 languages, including Chinese, Arabic, and Devanagari.
+[Pytesseract](https://pypi.org/project/pytesseract/) is a Python wrapper for [Google's Tesseract OCR engine](https://github.com/tesseract-ocr/tesseract), used for recognizing and extracting text from images. It works on a wide range of image types (e.g., JPEG, PNG, TIFF) and supports over 100 languages, including Chinese, Arabic, and Devanagari.
 
-Tesseract uses a character-level LSTM model and runs entirely on CPU, making it easy to deploy in low-resource environments. While it’s not state-of-the-art for complex layout or scene text, it’s fast, scriptable, and widely supported — ideal for lightweight OCR use cases.
+Tesseract uses a character-level LSTM model and runs entirely on CPU, making it easy to deploy in low-resource environments. While it's not state-of-the-art for complex layout or scene text, it's fast, scriptable, and widely supported — ideal for lightweight OCR use cases.
 
 ## Key features
 
@@ -104,13 +104,13 @@ Replace `"example.png"` with your own image file containing text. Pytesseract su
 | Good for simple forms and documents | Struggles with complex layouts or visual context |
 | CPU-only — works in low-resource environments | Lower accuracy than transformer-based models on cluttered or noisy inputs |
 
-Tesseract’s fast CPU performance and no-frills setup make it great for small-scale OCR, but it’s not optimized for high-volume pipelines or scene text recognition.
+Tesseract's fast CPU performance and no-frills setup make it great for small-scale OCR, but it's not optimized for high-volume pipelines or scene text recognition.
 
 ## When to use
 
 - You need fast OCR on clean documents or small image batches
 - You want to automate extraction from scanned forms, labels, or tables
-- You’re working in a CPU-only or resource-constrained environment
+- You're working in a CPU-only or resource-constrained environment
 - You want a scriptable fallback tool before reaching for ViT-based OCR
 
 ## See also