theyashwanthsai
commented
1 year ago @Helio-Centrism - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_datasetname.ipynb and link it in the readme of the labeled directory as algorithm - datasetname. :)
đź’ĄProject Proposal: GSSOC'23 - Handwritten Classification using CRNN
Introduction:
In this proposal for GSSOC'23, I present a project focused on handwritten classification using the CRNN (Convolutional Recurrent Neural Network) architecture. Handwritten character recognition is a fundamental problem in the field of computer vision and has numerous applications, including document analysis, digitizing historical archives, and automated form processing.
Objective:
The objective of this project is to develop an accurate and efficient handwritten character recognition system using the CRNN model. The proposed system will be capable of recognizing and classifying handwritten characters across various languages and writing styles.
Dataset: The data set will be taken from the Kaggle : https://www.kaggle.com/datasets/landlord/handwriting-recognition Tasks:
1.Data Preprocessing: The dataset will be preprocessed to extract individual character images and perform necessary augmentation techniques such as rotation, scaling, and noise addition to increase the model's robustness.
2.Model Development: The CRNN architecture, combining convolutional layers for feature extraction and recurrent layers for sequential modeling, will be implemented using deep learning frameworks such as TensorFlow or PyTorch. The model will be trained on the preprocessed dataset to learn the patterns and features of handwritten characters.
3.Training and Validation: The dataset will be split into training and validation sets. The model will be trained on the training set using appropriate loss functions and optimization algorithms, and its performance will be evaluated on the validation set.
Conclusion:
This project aims to develop a robust handwritten character recognition system using the CRNN architecture. By leveraging deep learning techniques and training on a diverse dataset, the proposed model will achieve high accuracy and be capable of recognizing handwritten characters from various writing styles and languages. This project has the potential to contribute to the field of document analysis, historical preservation, and automated form processing.