KHATT: A Deep Learning Benchmark on Arabic Script
Riaz Ahmad, Saeeda Naz, Muhammad Zeshan Afzal, Sheikh Faisal Rashid, Marcus Liwicki, Andreas R. Dengel · 2017
This work presents state-of-the-art results on one of the complex datasets; known as KHATT. The KHATT dataset shows complex patterns for Arabic handwritten text. We have achieved better performance in terms of Character Recognition by implementing the most successful deep learning approach based on Long Short-Term Memory (LSTM) networks. Connectionist Temporal Classification (CTC) is used as a final layer to align the predicted labels according to the most probable path. The application of MDLSTM scans text-lines in all direction to cover fine inflammation in horizontal and vertical direction. Further, we apply pre-processing on text-lines to prune extra white regions, and de-skew the text lines for accurate height normalization. The deep learning and pre-processing allow us to improve results from 46.13% to 75.8%.