https://github.com/kunald08/optical-character-recognition-using-deep-learning

Designed a model for detecting text handwriting from images of texts. Worked in keras using Convolutional Neural Networks(CNN) and Long Short Term Memory(LSTM) cells.
https://github.com/kunald08/optical-character-recognition-using-deep-learning

Last synced: 8 days ago
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Designed a model for detecting text handwriting from images of texts. Worked in keras using Convolutional Neural Networks(CNN) and Long Short Term Memory(LSTM) cells.

• Host: GitHub
• URL: https://github.com/kunald08/optical-character-recognition-using-deep-learning
• Owner: kunald08
• Created: 2024-10-05T07:56:19.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-05T07:58:58.000Z (3 months ago)
• Last Synced: 2024-12-22T13:42:05.469Z (25 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Optical-Character-Recognition-using-Deep-Learning

## Character Level

First download EMNIST dataset [here](http://www.nist.gov/itl/iad/image-group/emnist-dataset) 


Run **Char_rec.py**. Use the downloaded csv files for training and testing. 


Edit the code as per your framework to open the csv files. I used google colab so its written accordingly.

## Word Level

1. Download IAM dataset(word images and xml file) from [here](http://www.fki.inf.unibe.ch/databases/iam-handwriting-database)

2. Run **Images.py** to convert the images into a csv file. 


3. Run **Labels.py** to extract the labels from xml file and store it in a csv file. 


4. Run **Word_rec.py** to train the model using the csv files extracted above. 


Edit the code as per your framework to open the csv files. I used google colab so its written accordingly.