# 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 [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
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"description": "# Easy-to-Use Apple Vision wrapper for text extraction and clustering\n[](https://badge.fury.io/py/tensorflow) [](https://opensource.org/licenses/Apache-2.0) \n\n`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.\n\n\n\n## Features\n- **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.\n\n- **Clustering**: The tool can perform K-Means clustering on the extracted data. It groups similar text elements together based on their coordinates.\n\n- **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.\n\n## Dependencies\nThe script relies on the following Python libraries:\n- Torch\n- NumPy\n- Pandas\n- Pillow\n- Scikit-learn\n- Plotly\n- Pyobjc\n\n## Usage\nHere's how you can use `apple_ocr`:\n\n1. **Installation**: Install the required libraries, including `Torch`, `NumPy`, `Pandas`, `Pillow`, `scikit-learn`, and `Plotly`.\n\n2. **Initialization**: Create an instance of the `OCR` class, providing an image to be processed.\n```python\nfrom apple_ocr.ocr import OCR\nfrom PIL import Image\n\nimage = Image.open(\"your_image.png\")\nocr_instance = OCR(image=image)\n```\n\n3. **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.\n```python\ndataframe = ocr_instance.recognize()\n```\n\n4. **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.\n```python\ncluster_labels = ocr_instance.cluster(dataframe, num_clusters=3)\n```\n\n5. **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.\n```python\nocr_instance.scatter()\n```\n\n## Example\nHere's an example of the entire process:\n\n```python\nfrom apple_ocr.ocr import OCR\nfrom PIL import Image\n\nimage = Image open(\"your_image.png\")\nocr_instance = OCR(image=image)\ndataframe = ocr_instance.recognize()\ncluster_labels = ocr_instance.cluster(dataframe, num_clusters=3)\nocr_instance.scatter()\n```\n\n## Citing this project\nIf you use this code in your research, please use the following BibTeX entry.\n\n```BibTeX\n@misc{louisbrulenaudet2023,\n\tauthor = {Louis Brul\u00e9 Naudet},\n\ttitle = {Easy-to-Use Apple Vision wrapper for text extraction and clustering},\n\thowpublished = {\\url{https://github.com/louisbrulenaudet/apple-ocr}},\n\tyear = {2023}\n}\n\n```\n## Feedback\nIf you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).\n",
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