Towards Prompt Engineering and Large Language Models for Post-OCR correction in handwritten texts - Araújo, Sávio Santos de; Bezerra, Byron Leite Dantas; Sousa Neto, Arthur Flor de (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL), 2025)
This work explores the use of Large Language Models (LLMs) for post-OCR spelling correction in full sentences across Portuguese, French, and English. Using outputs from a state-of-the-art recognition model on the BRESSAY, RIMES, and IAM datasets, we evaluated two different zero-shot prompts. Closed LLMs, such as Gemini and GPT series, consistently outperform open-source models in reducing Character Error Rate (CER) and Word Error Rate (WER), while also offering faster inference. Despite the good accuracy of open models, their high computational demands hinder their practical use. Code is available at https://github.com/savi8sant8s/zero-shot-spelling-corrector .