https://github.com/sriram-merugu/image-processing-techniques-and-natural-language-processing
Gives Basic Code for some of important Image Processing Techniques Algorithms and NLP
https://github.com/sriram-merugu/image-processing-techniques-and-natural-language-processing
image-processing nlp
Last synced: about 1 year ago
JSON representation
Gives Basic Code for some of important Image Processing Techniques Algorithms and NLP
- Host: GitHub
- URL: https://github.com/sriram-merugu/image-processing-techniques-and-natural-language-processing
- Owner: Sriram-Merugu
- Created: 2024-06-23T19:51:00.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-17T20:01:19.000Z (over 1 year ago)
- Last Synced: 2025-05-30T20:44:19.258Z (about 1 year ago)
- Topics: image-processing, nlp
- Language: Jupyter Notebook
- Homepage:
- Size: 3.63 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# Project Title
## Description
This project contains Jupyter notebooks focused on various techniques in Image Processing and Natural Language Processing. The notebooks are organized into two main categories, each covering different algorithms and methodologies.
## Table of Contents
1. [Installation Instructions](#installation-instructions)
2. [Usage Instructions](#usage-instructions)
3. [Notebook Descriptions](#notebook-descriptions)
4. [Contributing](#contributing)
5. [License](#license)
## Installation Instructions
To set up the environment, ensure you have Python installed along with Jupyter Notebook. You can install the necessary libraries using pip
### Cloning the Repository
To clone the repository, use the following command:
```bash
git clone https://github.com/Sriram-Merugu/Image-Processing-Techniques-and-Natural-Language-Processing.git
```
## Usage Instructions
To execute the notebooks, you can use Jupyter Notebook or Google Colab.
### Running in Jupyter Notebook
1. Navigate to the directory containing the notebooks:
```bash
cd Image-Processing-Techniques-and-Natural-Language-Processing
```
2. Start Jupyter Notebook:
```bash
jupyter notebook
```
3. In the Jupyter interface, click on the notebook you want to run.
### Running in Google Colab
1. Open Google Colab in your web browser.
2. Click on "File" > "Upload notebook" to upload the notebook from your local machine.
3. Alternatively, you can open a notebook directly from GitHub by providing the repository link.
## Notebook Descriptions
### Image Processing Techniques
- **Applying Filters (Gaussian, Convolve, Median, etc.)**: Demonstrates various filtering techniques for image processing.
- **Canny Edge Detection**: Implements the Canny edge detection algorithm.
- **Dilation & Erosion**: Explains morphological operations in image processing.
- **Displaying Images through Different Methods**: Shows how to display images using various libraries.
- **JPEG IC**: Discusses JPEG image compression techniques.
- **Matrix Operations**: Covers basic matrix operations used in image processing.
- **Opening & Closing**: Explains morphological opening and closing operations.
- **Region of Interest (ROI)**: Demonstrates how to work with specific regions in images.
### Natural Language Processing
- **Minimum Edit Distance & POS Tagging**: Explores algorithms for calculating edit distance and part-of-speech tagging.
- **N-gram**: Discusses the N-gram model for text analysis.
- **Naïve Bayes Classifier & Evaluation**: Implements a Naïve Bayes classifier and evaluates its performance.
- **Naïve Bayes Classifier**: Provides a basic implementation of the Naïve Bayes algorithm.
- **Text & Word Normalization**: Covers techniques for normalizing text data.
## Contributing
Contributions are welcome! Please submit a pull request or open an issue for any suggestions or improvements.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.