Metadata-Version: 2.1
Name: Upender-recognizer
Version: 0.0.12
Summary: Converting handwritten (digits) information to digital format
Home-page: https://github.com/UpenderKaveti/Real-time-handwritten-digits-recognition-using-Convolutional-Neural-Network
Author: Upender_Kaveti
Author-email: artificalintelligence021@gmail.com
License: UNKNOWN
Keywords: handwritten,digits,recognition,OCR
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: licence.txt

# Real-time-handwritten-digits-recognition-using-Convolutional-Neural-Network

# Description
Reading handwritten information is still a difficult task for many of us, because each one of us is having a different interpretation style. As the world is moving towards digitization, converting the hand written information to a readable digital format reduces the difficulty. This approach will be beneficial for the readers as it gives a better understanding of the information. This packages deals with converting the real time handwritten digits to digital format with human level accuracy.

# Sample Working
![Diagram](https://user-images.githubusercontent.com/83408384/116879811-c51e9e00-ac3e-11eb-8397-f2f37c774aad.png)
 
# Sample Programming
* **pip install Upender-recognizer**

* **from jarvis.call import recognizer**

Downloading...

Weights retrieved

Downloading...

File (json) retrieved

**Give image_path :** /content/CamScanner 01-28-2022 10.44.26_1.jpg


*Note: Image path should be modified in case of jupyter notebook, spyder etc.*

Example:
image_path= "/content/CamScanner 01-28-2022 10.44.26_1.jpg"

updated_path= "//content//CamScanner 01-28-2022 10.44.26_1.jpg"


# Sample Output
![Capture](https://user-images.githubusercontent.com/83408384/116871250-9d750900-ac31-11eb-8293-dbf662b2cc66.PNG)


