# NeuSpell: A Neural Spelling Correction Toolkit

arXiv: https://arxiv.org/abs/2010.11085 · HTML: https://mlsanity.com/paper/2010.11085/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 30 of 32)

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- [DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2404.14890/) (2024)
- [A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages](https://mlsanity.com/paper/2308.09435/) (2023)
- [A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognition](https://mlsanity.com/paper/2212.14674/) (2022)
- [Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog](https://mlsanity.com/paper/2112.08327/) (2021)
- [BSpell: A CNN-Blended BERT Based Bangla Spell Checker](https://mlsanity.com/paper/2208.09709/) (2022)
- [RETVec: Resilient and Efficient Text Vectorizer](https://mlsanity.com/paper/2302.09207/) (2023)
- [Hierarchical Character Tagger for Short Text Spelling Error Correction](https://mlsanity.com/paper/2109.14259/) (2021)
- [Contextual Multilingual Spellchecker for User Queries](https://mlsanity.com/paper/2305.01082/) (2023)
- [TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation](https://mlsanity.com/paper/2505.08037/) (2025)
- [UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning](https://mlsanity.com/paper/2211.09717/) (2022)
- [NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models](https://mlsanity.com/paper/2303.10430/) (2023)
- [Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown Multi-Class Ensemble of CNNs](https://mlsanity.com/paper/2309.08146/) (2023)
- [HTEC: Human Transcription Error Correction](https://mlsanity.com/paper/2309.10089/) (2023)
- [Contextual Spelling Correction with Language Model for Low-resource Setting](https://mlsanity.com/paper/2404.18072/) (2024)
- [PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis](https://mlsanity.com/paper/2407.14789/) (2024)
- [Maps Search Misspelling Detection Leveraging Domain-Augmented Contextual Representations](https://mlsanity.com/paper/2108.06842/) (2021)
- [ABB-BERT: A BERT model for disambiguating abbreviations and contractions](https://mlsanity.com/paper/2207.04008/) (2022)
- [BiPhone: Modeling Inter Language Phonetic Influences in Text](https://mlsanity.com/paper/2307.03322/) (2023)
- [Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces](https://mlsanity.com/paper/2308.01976/) (2023)
- [Automatic Real-word Error Correction in Persian Text](https://mlsanity.com/paper/2407.14795/) (2024)
- [Improving the quality of Persian clinical text with a novel spelling correction system](https://mlsanity.com/paper/2408.03622/) (2024)
- [An Open-Source American Sign Language Fingerspell Recognition and Semantic Pose Retrieval Interface](https://mlsanity.com/paper/2408.09311/) (2024)

## Abstract (arXiv)

We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use contextual representations. By training on our synthetic examples, correction rates improve by 9% (absolute) compared to the case when models are trained on randomly sampled character perturbations. Using richer contextual representations boosts the correction rate by another 3%. Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings. The toolkit can be accessed at neuspell.github.io. Code and pretrained models are available at http://github.com/neuspell/neuspell.