  Images of handwritten digits are different from natural images as the
orientation of a digit, as well as similarity of features of different digits,
makes confusion. On the other hand, deep convolutional neural networks are
achieving huge success in computer vision problems, especially in image
classification. BDNet is a densely connected deep convolutional neural network
model used to classify (recognize) Bengali handwritten numeral digits. It is
end-to-end trained using ISI Bengali handwritten numeral dataset. During
training, untraditional data preprocessing and augmentation techniques are used
so that the trained model works on a different dataset. The model has achieved
the test accuracy of 99.775%(baseline was 99.40%) on the test dataset of ISI
Bengali handwritten numerals. So, the BDNet model gives 62.5% error reduction
compared to previous state-of-the-art models. Here we have also created a
dataset of 1000 images of Bengali handwritten numerals to test the trained
model, and it giving promising results. Codes, trained model and our own
dataset are available at: {https://github.com/Sufianlab/BDNet}.
