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grapheme-kit addresses a critical gap in multilingual NLP by shifting evaluation metrics from Unicode code points to grapheme clusters, ensuring accurate error measurement for complex scripts like Tamil and Sinhala. This open-source library enhances the fidelity of text processing pipelines, particularly for Optical Character Recognition systems handling non-Latin writing systems. By resolving representation mismatches inherent in standard encoding schemes, it provides a more robust foundation for cross-lingual model development.
grapheme-kit: Grapheme-Level Metrics and Text Processing for Multilingual NLP
arXiv:2607.22456v1 Announce Type: new 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/decomposition utilities. Through an OCR case study, we demonstrate that grapheme-level metrics provide a more faithful evaluation of complex scripts.
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/decomposition utilities. Through an OCR case study, we demonstrate that grapheme-level metrics provide a more faithful evaluation of complex scripts.
Computation and Language (cs.CL)
arXiv:2607.22456 [cs.CL]
(or arXiv:2607.22456v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.22456 Focus to learn more arXiv-issued DOI via DataCite
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