# PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis

arXiv: https://arxiv.org/abs/2407.14789 · HTML: https://mlsanity.com/paper/2407.14789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 81 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://mlsanity.com/paper/2305.18156/) (2023)
- [NeuSpell: A Neural Spelling Correction Toolkit](https://mlsanity.com/paper/2010.11085/) (2020)
- [Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems](https://mlsanity.com/paper/2203.00888/) (2022)
- [Persian Handwritten Digit, Character and Word Recognition Using Deep Learning](https://mlsanity.com/paper/2010.12880/) (2020)
- [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://mlsanity.com/paper/2301.06323/) (2023)
- [AraSpell: A Deep Learning Approach for Arabic Spelling Correction](https://mlsanity.com/paper/2405.06981/) (2024)
- [Automatic Standardization of Colloquial Persian](https://mlsanity.com/paper/2012.05879/) (2020)
- [Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence](https://mlsanity.com/paper/2302.04096/) (2023)
- [Detection is the central problem in real-word spelling correction](https://mlsanity.com/paper/1408.3153/) (2014)
- [Correcting Real-Word Spelling Errors: A New Hybrid Approach](https://mlsanity.com/paper/2302.06407/) (2023)

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

- [Incorporating Error Level Noise Embedding for Improving LLM-Assisted Robustness in Persian Speech Recognition](https://mlsanity.com/paper/2512.17247/) (2025)

## Abstract (arXiv)

This research introduces a state-of-the-art Persian spelling correction system that seamlessly integrates deep learning techniques with phonetic analysis, significantly enhancing the accuracy and efficiency of natural language processing (NLP) for Persian. Utilizing a fine-tuned language representation model, our methodology effectively combines deep contextual analysis with phonetic insights, adeptly correcting both non-word and real-word spelling errors. This strategy proves particularly effective in tackling the unique complexities of Persian spelling, including its elaborate morphology and the challenge of homophony. A thorough evaluation on a wide-ranging dataset confirms our system's superior performance compared to existing methods, with impressive F1-Scores of 0.890 for detecting real-word errors and 0.905 for correcting them. Additionally, the system demonstrates a strong capability in non-word error correction, achieving an F1-Score of 0.891. These results illustrate the significant benefits of incorporating phonetic insights into deep learning models for spelling correction. Our contributions not only advance Persian language processing by providing a versatile solution for a variety of NLP applications but also pave the way for future research in the field, emphasizing the critical role of phonetic analysis in developing effective spelling correction system.