holistic persian handwritten word recognition using convolutional neural network

Due to the cursive-ness and high variability of persian script, the segmentation of handwritten words into sub-words is still a challenging task. these issues could be addressed in a holistic approach by sidestepping segmentation at the character level. in this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line persian handwritten words. the proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in the required parameters. the effect of various pooling strategies is also investigated in this paper. the primary goal of this article is to ignore handcrafted feature extraction and to attain a generalized and stable word recognition system. the presented model is assessed using two famous handwritten persian word databases called sadri and iranshahr. the recognition accuracies were obtained at 98.6% and 94.6%, on sadri and iranshahr datasets respectively, and outperformed the state-of-the-art methods.