L'IA peut-elle lire l'écriture manuscrite dans plus de 50 écritures ?

Latin, cyrillique, devanagari, arabe, sinogrammes, hangul — les OCR modernes gèrent essentiellement tous les systèmes d'écriture avec une précision globalement acceptable.

Background

Modern OCR systems can already process handwriting in Latin, Cyrillic, Devanagari, Arabic, Han (Chinese/Japanese/Korean) characters, and Hangul with generally acceptable accuracy. Current AI systems have made substantial advances in recognizing handwriting across multiple scripts, supported by both commercial and open-source libraries. However, scaling this capability to 50+ distinct writing systems remains a research frontier due to the vast diversity in writing styles, font variability, and intricate linguistic structures. Deep learning techniques—especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—have driven significant improvements in multilingual handwriting recognition. While state-of-the-art models perform robustly in major scripts such as Latin, Chinese, and Arabic, extending reliable OCR to over 50 scripts demands continued innovation in model generalization and cross-script adaptation. This challenge persists despite progress in large-scale pretrained models and multilingual text corpora.

Source: International Journal of Document Analysis and Recognition (enriched May 9, 2026)