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https://github.com/louisbrulenaudet/apple-ocr
Easy-to-Use Apple Vision wrapper for text extraction, scalar representation and clustering using K-means.
https://github.com/louisbrulenaudet/apple-ocr
apple clustering kmeans nlp ocr ocr-recognition pyobjc python scatter-plot sklearn
Last synced: about 1 month ago
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Easy-to-Use Apple Vision wrapper for text extraction, scalar representation and clustering using K-means.
- Host: GitHub
- URL: https://github.com/louisbrulenaudet/apple-ocr
- Owner: louisbrulenaudet
- License: apache-2.0
- Created: 2023-10-20T12:50:46.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-29T19:26:55.000Z (10 months ago)
- Last Synced: 2024-09-27T20:22:48.704Z (about 2 months ago)
- Topics: apple, clustering, kmeans, nlp, ocr, ocr-recognition, pyobjc, python, scatter-plot, sklearn
- Language: Python
- Homepage: https://github.com/louisbrulenaudet/apple-ocr
- Size: 146 KB
- Stars: 86
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
README
# Easy-to-Use Apple Vision wrapper for text extraction and clustering
[](https://badge.fury.io/py/tensorflow) [](https://opensource.org/licenses/Apache-2.0) 
`apple_ocr` is a utility for Optical Character Recognition (OCR) that facilitates the extraction of text from images. This Python-based tool is designed to help developers, researchers, and enthusiasts in the field of text extraction and clustering. It leverages a combination of various technologies to achieve this, including the Vision framework provided by Apple.
## Features
- **Text Recognition**: `apple_ocr` uses the Vision framework to recognize text within an image. It extracts recognized text and provides information about its confidence levels.
- **Clustering**: The tool can perform K-Means clustering on the extracted data. It groups similar text elements together based on their coordinates.
- **Interactive 3D Visualization**: `apple_ocr` offers an interactive 3D scatter plot using Plotly, displaying the clustered text elements. This visualization helps users gain insights into the distribution of text and text density.
## Dependencies
The script relies on the following Python libraries:
- Torch
- NumPy
- Pandas
- Pillow
- Scikit-learn
- Plotly
- Pyobjc
## Usage
Here's how you can use `apple_ocr`:
1. **Installation**: Install the required libraries, including `Torch`, `NumPy`, `Pandas`, `Pillow`, `scikit-learn`, and `Plotly`.
2. **Initialization**: Create an instance of the `OCR` class, providing an image to be processed.
```python
from apple_ocr.ocr import OCR
from PIL import Image
image = Image.open("your_image.png")
ocr_instance = OCR(image=image)
```
3. **Text Recognition**: Use the `recognize` method to perform text recognition. It will return a structured DataFrame containing recognized text, bounding box dimensions, text density, and centroid coordinates.
```python
dataframe = ocr_instance.recognize()
```
4. **Clustering**: Use the `cluster` method to perform K-Means clustering on the recognized text data. This method assigns cluster labels to each data point based on their coordinates.
```python
cluster_labels = ocr_instance.cluster(dataframe, num_clusters=3)
```
5. **Visualization**: Finally, use the `scatter` method to create an interactive 3D scatter plot. This plot visualizes the clustered text elements, including centroids, text density, and more.
```python
ocr_instance.scatter()
```
## Example
Here's an example of the entire process:
```python
from apple_ocr.ocr import OCR
from PIL import Image
image = Image.open("your_image.png")
ocr_instance = OCR(image=image)
dataframe = ocr_instance.recognize()
cluster_labels = ocr_instance.cluster(dataframe, num_clusters=3)
ocr_instance.scatter()
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Easy-to-Use Apple Vision wrapper for text extraction and clustering},
howpublished = {\url{https://github.com/louisbrulenaudet/apple-ocr}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).