KPTI: Katib’s Pashto Text Imagebase and DeepLearning Benchmark

Riaz Ahmad; M. Zeshan Afzal; S. Faisal Rashid; Marcus Liwicki; Thomas Breuel; Andreas Dengel

In: 15th International Conference on Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 2016.


This paper presents the first Pashto text image database for scientific research and thereby the first dataset with complete handwritten and printed text line images which ultimately covers all alphabets of Arabic and Persian languages. Language like Pashto, written in a complex way by calligraphers, still requires a mature Optical Character Recognition (OCR), system. Although 50 million people use this language both for oral and written communication, there is no significant effort which is devoted to the recognition of Pashto Script. A real dataset of 17,015 images having Pashto text lines is introduced. The images are acquired via scanning from hand scribed Pashto books.Further, in this work, we evaluated the performance of deep learning based models like Bidirectional and Multi-Dimensional Long Short Term Memory (BLSTM and MDLSTM) networks for Pashto texts and provide a baseline character error rate of 9.22%.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence