# DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages

arXiv: https://arxiv.org/abs/2211.03730 · HTML: https://mlsanity.com/paper/2211.03730/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)
- [A context sensitive real-time Spell Checker with language adaptability](https://mlsanity.com/paper/1910.11242/) (2019)
- [Spell Correction for Azerbaijani Language using Deep Neural Networks](https://mlsanity.com/paper/2102.03218/) (2021)
- [SinSpell: A Comprehensive Spelling Checker for Sinhala](https://mlsanity.com/paper/2107.02983/) (2021)

## Built on by (most cited first, 3 of 3)

- [TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation](https://mlsanity.com/paper/2505.08037/) (2025)
- [Semantically Corrected Amharic Automatic Speech Recognition](https://mlsanity.com/paper/2404.13362/) (2024)
- [VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction](https://mlsanity.com/paper/2609.36804/) (2026)

## Abstract (arXiv)

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.