A spell checker that actually reads the sentence

What it does

NeuSpell is an open-source toolkit for context-sensitive spelling correction in English. It packages eight neural correctors—from CNN-LSTM hybrids to BERT and ELMO variants—behind a unified API, plus pretrained checkpoints and a small Flask demo. The toolkit also generates synthetic training data by injecting realistic noise into clean sentences so models learn to fix typos in context.

The interesting bit

Instead of treating spell-checking as a dictionary lookup, NeuSpell trains models on sentences where roughly 20% of tokens are deliberately corrupted using word-level, character-level, or probabilistic confusion-matrix noise. This reverse-engineering of isolated misspellings into full context is the paper’s central trick, and it pushes word-correction rates on the BEA-60K benchmark to nearly 80%, well above Aspell’s 49%.

Key highlights

• Eight neural checkers in one toolkit, including BERT, SC-LSTM, and ELMO hybrids.
• Synthetic noising APIs for English: word replacement, character swaps, and probabilistic confusion-matrix corruption.
• Benchmarked on real-world misspellings (BEA-60K); top model hits 79.8% word correction rate.
• Accepted at EMNLP 2020; includes a live web demo and Hugging Face integration for the BERT model.
• Checkpoints range from ~450 MB to 1.23 GB.

Caveats

• English-only; synthetic data creation and pretrained models are not multilingual.
• ELMO-based checkers require a source install because allennlp is excluded from the pip package.
• The bundled Flask demo requires manually editing imports if you only want to load one model.

Verdict

NLP practitioners building English pipelines or researching adversarial misspellings should look here. If you need lightweight, multilingual, or real-time CPU-only correction, this is probably overkill.