No, these are completely different, standalone code points, not variant forms of the same code point. What's actually happening seems to be that the ch_tra mod

No, these are completely different, standalone code points, not variant forms of the same code point. What's actually happening seems to be that the ch_tra model can recognize simplified too and output the corresponding traditional version if the character isn't in the traditional "alphabet"; it doesn't work so well in the other direction. Example recognizing a partial screenshot of https://chinese.stackexchange.com/a/38707 https://chinese.stackexchange.com/a/38707 (anyone can try this on Google Colab, no hardware required; remember to turn on GPU in Runtime -> Change runtime type): import easyocr import requests zhs_reader = easyocr.Reader(['en', 'ch_sim']) zht_reader = easyocr.Reader(['en', 'ch_tra']) image = requests.get('https://i.imgur.com/HtrpZCZ.png').content print('ch_sim:', ' '.join(text for _, text, _ in zhs_reader.readtext(image))) print('ch_tra:', ' '.join(text for _, text, _ in zht_reader.readtext(image))) Results: ch_sim: One simplified character may mapping to multiple traditional ones: 皇后->皇后,後夭->后夭 豌鬟->头发,骏财->发财 As reversed, one traditional character may mapping to multiple simplified ones too: 乾燥->干燥, 乾隆->乾隆 嘹望->嘹望,嘹解->了解 ch_tra: One simplified character may mapping to multiple traditional ones: 皇后->皇后,後天->后天 頭髮->頭發,發財->發財 As reversed, one traditional character may mapping to multiple simplified ones too: 乾燥->干燥, 乾隆->乾隆 瞭望->瞭望, 瞭解->了解 Compare to the original text: One simplified character may mapping to multiple traditional ones: - 皇后 -> 皇后，後天 -> 后天 - 頭髮 -> 头发，發財 -> 发财 As reversed, one traditional character may mapping to multiple simplified ones too: - 乾燥 -> 干燥，乾隆 -> 乾隆 - 瞭望 -> 瞭望，瞭解 -> 了解 Of course, automatic character-to-character conversion from simplified to traditional can be wrong due to ambiguities; excellent examples from above: 头发 => 頭發 (should be 頭髮), 了解 => 了解 (should be 瞭解).