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How LectuLibre Built a Multi-Model System to Fix AI Book Translation Errors

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LectuLibre, an AI-powered book translation service, found that its initial language detection using Python's langdetect library was accurate only about 85% of the time on real-world EPUB and PDF uploads. The 15% error rate caused garbled translations, user frustration, and wasted API costs, prompting the team to build a more robust solution. They developed a two-tier detection system combining fastText, langdetect, cld3, and a custom fine-tuned model trained on 10,000 multilingual book excerpts. A majority-vote mechanism across these four detectors determines the source language, with confidence scoring used as a tiebreaker and user-specified language serving as an override with mismatch warnings. The team plans to open-source their custom fine-tuned fastText model trained specifically on book-style text.

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How LectuLibre Built a Multi-Model System to Fix AI Book Translation Errors · ShortSingh