# Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters

arXiv: https://arxiv.org/abs/1806.08037 · HTML: https://mlsanity.com/paper/1806.08037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Noise robust neural network architecture](https://mlsanity.com/paper/2305.09276/) (2023)

## Abstract (arXiv)

Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconstructing and classifying a noisy version of handwritten Bangla Numeral and Basic Character datasets.