ayan-cs / handwritten-character-recognition-banglalekha

Handwritten Bangla Character Classification using ResNet-34 trained using BanglaLekha Dataset. System has been implemented in PyTorch. For details, see the README file.

Handwritten Bangla Character Recognition using CNN in PyTorch

About the dataset

The Original Dataset contains Handwritten Bangla Characters and Digits, a total of 84 classes where,

• 11 are Vowels
• 37 are consonants
• Rest are some of the Complex characters (consisting of 2 or more graphemes)

Instructions for Training the model

• Open CMD/Terminal and clone the repository using the command : git clone [email protected]:ayan-cs/handwritten-character-recognition-banglalekha
• Download the BanglaLekha Numerals dataset from the given link above and extract inside the repository folder. It is recommended not to make any change to the dataset folder.
• Preprocess the data by executing the Data_Preparation.ipynb notebook. This should create Train and Validation splits inside the parent dataset folder. (If you want to reverse the split and re-split the dataset again, a code snippet is available inside the notebook)
• Configure train_config.yaml file.
• Run the script on CMD/Terminal : python main.py train
• The trained model will be available inside Checkpoints folder and the plots will be saved inside Plots & Outputs folder.

Instructions for Inference/Prediction

• Open CMD/Terminal and clone the repository using the command : git clone [email protected]:ayan-cs/handwritten-character-recognition-banglalekha
• Make sure the data is preprocessed.
• Configure inference_config.yaml file. For demo, one trained ResNet-34 model has been provided.
• Open CMD/Terminal, run the command : python main.py inference
• The outputs will be available inside the Plots & Outputs folder.