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habib.ibrahim · PyLaia · Published August 2, 2026

Agapet 5.0

Text Recognition

Description

An updated version of Agapet trained with the handwriting of the following copyists: Antoine Dawud (VA 71, BNU Or. 4,226) Stephan al-Ramli (877, BL 4950) Bishr Ibn al-Sirri (867, SA 151) Thomas of Fustat (900, BNU Or. 4225a) Michael the deacon (901, NF M 7) Dawud al-Asqalani (909, SA 309) Daniel ibn Arsenius (917, BL or 5008) Dawud of Homs (10th c., Tischend. ) Yusuf (10th c., Milan X 199 sup.) John of Damietta (984/5, SA 116) Peter of Saint Simon? (11th c., SA 69 ) Simeon (11th c. , SA NF M 53 ) Meletius of Archaia (1186, SA 356) Niqūlā ibn Abul-Surūr (1095) – SA 417 Poimen al-siqi (1223) – SA 418 Gawan ibn Dimitri (1258) – SA 398, 403 Habib ibn Macarius (15th c.) – SA 314 David ibn Tadrus (16th c., VA 426) Thalga Karma (17th c.) – OBC Simon of Homs (17th c.) – SA 423 Marqus of Aleppo (17th c.) - OBC Constantin ibn Dawud (19th c., Cambridge) Unknown (9th-17th, Milan X 198, Vat 13, 14, SA 1, 2, 13, 154, 350, 508, 514)

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Use this modelOpen in Transkribus
Low error rate7.78% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 7.78% on its validation set. As a rule of thumb, a CER below 10% is considered good for most handwritten material. This is a larger model trained on diverse material, which generally makes it more robust across different handwriting styles. That said, larger training sets also make it harder to push the CER down further.

Measured on the model's own validation data. Results on your documents may differ depending on handwriting style, document condition, language, and how closely your material resembles the training data.

Words644,590
Lines74,104
Training Pages2,745
Model ID612549
Languages
Arabic
Centuries
9th c.10th c.11th c.12th c.13th c.14th c.15th c.16th c.17th c.18th c.19th c.