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Handwritten Bangla Numeric Digits Classification using ResNet-34. The work uses a subset of the BanglaLekha-Isolated dataset. For details, read the README file.

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ayan-cs/banglalekha-numerals-classification

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BanglaLekha Handwritten Digits Classification

About the dataset

The Original Dataset contains Handwritten Bangla Characters and Digits. Here, only the Handwritten Digits have been taken into consideration. The dataset used here is available here.

Instructions for Training the model

  • Open CMD/Terminal and clone the repository using the command : git clone [email protected]:ayan-cs/banglalekha-numerals-classification
  • 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 a folder Preprocessed_Dataset containing Train and Validation splits.
  • 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/banglalekha-numerals-classification
  • 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.

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Handwritten Bangla Numeric Digits Classification using ResNet-34. The work uses a subset of the BanglaLekha-Isolated dataset. For details, read the README file.

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