Subword recognition in historical Arabic manuscripts using handcrafted features and deep learning approaches

Recent years have seen significant endeavors to improve handwriting recognition systems and digitize historical manuscripts. Nevertheless, recognizing historical Arabic manuscripts remains a considerable challenge. The purpose of this study is to investigate subword recognition in historical Arabic manuscripts. Two systems are established. The first system involves using a variety of handcrafted feature methods with diverse machine learning algorithms. The second system uses a deep learning architecture that integrates convolutional neural network and bidirectional long short-term memory based on a character model approach with connectionist temporal classification as a decoder. By utilizing the IBN SINA dataset, the histogram of oriented gradients descriptor demonstrated superior performance in the first system, while the second system achieved notable results. The findings of this study provide a framework for the development of historical manuscript recognition systems.