update readme

README.md

@@ -0,0 +1,69 @@
+# OCR Web Application
+
+## Project Overview
+This is a **web-based Optical Character Recognition (OCR) application** built using Streamlit. The app supports both English and Hindi languages, allowing users to upload images and extract text using advanced OCR models.
+
+## How the Application Works
+1. Choose Language: Select either English or Hindi using the sidebar instructions.
+2. Upload Image: Use the file uploader to input an image in JPG, PNG, or JPEG format.
+3. Text Extraction: For English, the app uses the GOT OCR 2.0 model to extract text, while for Hindi, it leverages EasyOCR.
+4. Keyword Search: After text extraction, you can search for specific keywords within the extracted text. Matching keywords will be highlighted, and any missing keywords will be displayed in a warning message.
+5. Reset: If needed, reset the session and upload a new image to start over.
+
+## Installation and Setup
+
+### Prerequisites:
+- **Python 3.8 or higher**
+- Required libraries listed in `requirements.txt`
+
+### Installation Steps:
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/Trisandhyadevi/OCR.git
+
+2. **Navigate to the project directory**
+   ```bash
+    cd OCR
+
+3. **Install the required dependencies:**
+    ```bash
+    pip install -r requirements.txt
+
+4. **Run the application:**
+    ```bash
+    streamlit run app.py
+
+
+# Description
+
+This web application supports converting images to text using the GOT OCR 2.0 Model. Below are some key features of the GOT OCR 2.0 model
+
+# GOT OCR 2.0 Model Overview
+
+The GOT OCR 2.0 Model is a state-of-the-art OCR system designed for accurate text extraction from images. Key features include:
+
+- **Multi-task Learning**: The model supports various tasks beyond OCR, including layout analysis and object detection, making it versatile for diverse text recognition needs.
+- **End-to-End Pipeline**: It efficiently processes entire images, identifying and extracting text without the need for additional preprocessing steps.
+
+Note: Currently, the model does not support all languages. Fine-tuning is required for languages not included in the pre-trained model. For more information on fine-tuning, visit the [GOT OCR 2.0 Fine-tuning Guide](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/?tab=readme-ov-file#fine-tune).
+
+For more technical details about the model architecture and usage, visit the [GOT OCR 2.0 Model Documentation](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/?tab=readme-ov-file#general-ocr-theory-towards-ocr-20-via-a-unified-end-to-end-model).
+
+
+## Deployment
+To deploy the application to a cloud platform(Hugging Face)
+
+## Folder Structure
+ 1.```bash
+        .
+        ├── app.py                # Main application file
+        ├── requirements.txt       # Python dependencies
+        └── README.md              # Projectdocumentation
+
+
+## Dependencies
+1. Streamlit: Web framework to create the interactive interface.
+2. Transformers: To load the GOT OCR 2.0 model.
+3. EasyOCR: To process Hindi text extraction.
+4. Torchvision: To handle image transformations.
+5. Pillow: Image processing library.

app.py

@@ -75,6 +75,15 @@ if 'reset' not in st.session_state:
 
 if 'language' not in st.session_state:
     st.session_state.language = False
+    
+with st.sidebar:
+    st.header("Instructions")
+    st.write("1. Choose a language (English or Hindi).")
+    st.write("2. Upload an image in JPG, PNG, or JPEG format.")
+    st.write("3. The app will extract text from the image using OCR.")
+    st.write("4. Enter keywords to search within the extracted text.")
+    st.write("5. If needed, click 'Reset' to upload a new image.")
+
 
 st.header("Optical Character Recognition ")
 col1, col2 = st.columns(2)