grapheme-kit: Grapheme-Level Metrics and Text Processing for Multilingual NLP
Abstract
Existing lexical distance, similarity, and evaluation metrics operate on Unicode code points, which can misrepresent errors in writing systems where a single grapheme is represented by multiple Unicode code points. We introduce grapheme-kit, an open-source Python library that extends these metrics to operate on grapheme clusters instead. The library also provides improved grapheme processing for Tamil and Sinhala, including accurate grapheme cluster identification and grapheme composition/decomp...
Description / Details
Existing lexical distance, similarity, and evaluation metrics operate on Unicode code points, which can misrepresent errors in writing systems where a single grapheme is represented by multiple Unicode code points. We introduce grapheme-kit, an open-source Python library that extends these metrics to operate on grapheme clusters instead. The library also provides improved grapheme processing for Tamil and Sinhala, including accurate grapheme cluster identification and grapheme composition/decomposition utilities. Through an OCR case study, we demonstrate that grapheme-level metrics provide a more faithful evaluation of complex scripts.
Source: arXiv:2607.22456v1 - http://arxiv.org/abs/2607.22456v1 PDF: https://arxiv.org/pdf/2607.22456v1 Original Link: http://arxiv.org/abs/2607.22456v1
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Jul 27, 2026
Computational Linguistics
NLP
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