v1.0-beta

Release Notes

Convolutional Neural Network (CNN) Model - Version 1.0.0-beta

Description:

This release introduces my initial Convolutional Neural Network (CNN) model tailored for Hindi OCR. With simple-yet-accurate architecture aiming to be a mobile model, the model is designed to capture spatial hierarchies and is highly efficient for image data.

Key Features:

• Tailored for Hindi character recognition.
• Utilizes multiple convolutional and pooling layers.
• Improved accuracy on validation dataset.
• Changes from Previous Version:

Initial release.
Model Performance Metrics:
Training accuracy: 94% | Accuracy on unseen samples: 97%

Dependencies:

          -  _Tensorflow_
                  - Version: 2.14.0
                  - Summary: TensorFlow is an open source machine learning framework for everyone.
                  - Home-page: https://www.tensorflow.org/
                  - Author: Google Inc.
                  - Author-email: [email protected]
                  - License: Apache 2.0
                  - Location: C:\Users\Sanju\AppData\Local\Programs\Python\Python311\Lib\site-packages
                  - Requires: tensorflow-intel

        - _Keras_
              - Name: keras
              - Version: 2.14.0
              - Summary: Deep learning for humans.
              - Home-page: https://keras.io/
              - Author: Keras team
              - Author-email: [email protected]
              - License: Apache 2.0
              - Location: C:\Users\Sanju\AppData\Local\Programs\Python\Python311\Lib\site-packages
              - Required-by: tensorflow-intel

Model Architecture:

Recurrent Neural Network (RNN) Model - Version 1.0.0-beta

Description:

Presenting a Recurrent Neural Network (RNN) model for Hindi OCR. Crafted for sequences, this model excels in recognizing patterns over time and space.

Key Features

• Adapted for Hindi character sequences.
• Incorporates SimpleRNN layers to process sequences.
• Enhanced performance metrics on the validation set.
• Changes from Previous Version:

Initial release.
Model Performance Metrics:
Training accuracy: 88% | Accuracy on unseen samples: 90%

Model Architecture:

Next Up: v0.2-beta

The next milestone is to create an API that can use these models, and make it simple for method calling and classification.

@Khurdhula-Harshavardhan