Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining
This work addresses the problem of low accuracy in online Arabic handwriting recognition for users in fields like document digitization, though it appears incremental as it builds on existing methods with specific improvements.
The paper tackled the challenge of online Arabic cursive character recognition by proposing an enhanced method for extracting critical points from stroke features and using a multilayer perceptron for classification, achieving an average accuracy of 98.6%.
Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic character's recognition is still low and retain space for improvement. In this research, an enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. Each extracted stroke feature divides every isolated character into some meaningful pattern known as tokens. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. In this work, two milestones are achieved; firstly, attain a fixed number of tokens, secondly, minimize the number of the most repetitive tokens. For experiments, handwritten Arabic characters are selected from the OHASD benchmark dataset to test and evaluate the proposed method. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.