Converting handwritten (digits) information to digital format
Project 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
Sample Programming
-
pip install Upender-recognizer
-
from jarvis.call import recognizer
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file Upender_recognizer-0.0.14.tar.gz.
File metadata
- Download URL: Upender_recognizer-0.0.14.tar.gz
- Upload date:
- Size: 4.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d8eb2aaedf9d8ff6e5cbccd72b1ea5e1e95535632354e8fb384c8c415da214d8
|
|
| MD5 |
2f92a6f415c45507ae169d4f417a13dd
|
|
| BLAKE2b-256 |
abb3231e1d012436884fa48054cd0da7cba3690c2aa7b5363794e7ea0277cfd1
|
File details
Details for the file Upender_recognizer-0.0.14-py3-none-any.whl.
File metadata
- Download URL: Upender_recognizer-0.0.14-py3-none-any.whl
- Upload date:
- Size: 4.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f3b154be38fcbe852af4ec66bd50dd7466773268ccd1bb3c5812b938dfef6b33
|
|
| MD5 |
f7b24801b08cc4ce914e4ccc82760ae7
|
|
| BLAKE2b-256 |
d3d78e29910bf2ec4f868b96866dd7a0c85885c5e5c158f4539157d71322826e
|
