A novel handwritten Turkish letter recognition model based on convolutional neural network

Abstract

AbstractConvolutional neural networks have provided state‐of‐the‐art solutions for many subfields of computer vision. While there exist many studies in the literature for several languages, studies for handwritten Turkish character recognition lack in the research field. To this end, we propose a novel handwritten Turkish letter recognition model based on a convolutional neural network. Since, to the best of our knowledge, there do not exist any publicly available handwritten Turkish letters datasets, we constructed a handwritten Turkish letters dataset that consists of 25,875 samples. To compare the performance of the proposed model with the related work, three state‐of‐the‐art models, namely, VGG19, InceptionV3, and Xception, were utilized through the transfer learning technique. When these models were evaluated on the handwritten Turkish letter dataset, the proposed model's accuracy was calculated as high as 96.07% which was higher than the benchmark models. To measure the generalization ability of the proposed model, it was evaluated on a gold standard dataset, namely, EMNIST, and has achieved an accuracy of 80.54% which was higher than the benchmark models. Finally, the proposed model was trained and evaluated on the EMNIST dataset and it has achieved an accuracy of 94.61% which outperformed the related work.