Update README.md

README.md

@@ -37,6 +37,8 @@ This model is particularly effective in **retrieving mathematical notations and
 
 ![latexqwen.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-h5z3giEudPrdM9qRMMTe.png)
 
+Demo: https://huggingface.co/prithivMLmods/LatexMind-2B-Codec/blob/main/latexmind/latexmind-codec.ipynb
+
 # Use it with Transformers
 
 
@@ -102,7 +104,7 @@ output_text = processor.batch_decode(
 )
 print(output_text)
 ```
-### Buf
+# Buf
 ```python
     buffer = ""
     for new_text in streamer:
@@ -111,5 +113,30 @@ print(output_text)
         buffer = buffer.replace("<|im_end|>", "")
         yield buffer
 ```
+Here’s the **Intended Use & Limitations** section for **LatexMind-2B-Codec**:  
+
+---
+
+# Intended Use 
+
+**LatexMind-2B-Codec** is designed for tasks that require **image-based text recognition**, **math equation extraction**, and **multi-modal understanding**. It is particularly useful in the following scenarios:  
+
+🔹 **Optical Character Recognition (OCR)** – Extracting printed and handwritten text from images, documents, and scanned pages.  
+🔹 **Math Expression Recognition** – Converting mathematical notations into structured **LaTeX format** for further computation and documentation.  
+🔹 **Image-to-Text Conversion** – Generating accurate descriptions for text-rich and math-heavy images.  
+🔹 **Document and Academic Processing** – Assisting researchers, students, and professionals in digitizing handwritten notes and extracting structured content from books, PDFs, and whiteboards.  
+🔹 **Automated Educational Support** – Enabling AI-powered tutors, content summarization, and interactive learning for subjects involving complex equations.  
+🔹 **Multi-Language OCR** – Recognizing text inside images across multiple languages, including English, Chinese, Japanese, Korean, Arabic, and various European languages.  
+🔹 **Video-Based Question Answering** – Understanding long-duration videos for content summarization, question answering, and structured data extraction.  
+
+# Limitations 
 
+Despite its capabilities, **LatexMind-2B-Codec** has some inherent limitations:  
 
+⚠ **Handwritten Text Accuracy** – While it can recognize handwritten equations, performance may degrade with highly unstructured or messy handwriting.  
+⚠ **Complex LaTeX Formatting** – The model may struggle with deeply nested or ambiguous LaTeX expressions, requiring manual corrections for precise formatting.  
+⚠ **Low-Resolution Images** – Extracting accurate text from blurry or low-resolution images can lead to misinterpretations or OCR errors.  
+⚠ **Contextual Understanding in Multi-Step Equations** – While it recognizes math expressions, solving multi-step problems autonomously may be limited.  
+⚠ **Limited Support for Rare Mathematical Notations** – Some specialized or domain-specific symbols may not be recognized with high accuracy.  
+⚠ **Processing Speed for Large Documents** – Performance may slow down when handling extremely large documents or dense mathematical content in real-time applications.  
+⚠ **Language-Specific OCR Variability** – While it supports multiple languages, OCR accuracy may vary depending on the script complexity and font style.