ACCENTS Transactions on Image Processing and Computer Vision, Vol 6(21) ISSN (Online): 2455-4707 http://dx.doi.org/10.19101/TIPCV.2020.618051 68 Deep ensemble neural networks for recognizing isolated Arabic handwritten characters Haifa Alyahya 1* , Mohamed Maher Ben Ismail 2 and AbdulMalik Al-Salman 3 Lecturer, Department of Computer Science, King Saud University, Saudi Arabia 1 Associate Professor, Department of Computer Science, King Saud University, Saudi Arabia 2 Professor, Department of Computer Science, King Saud University, Saudi Arabia 3 Received: 20-August-2020; Revised: 25-October-2020; Accepted: 05-November-2020 ©2020 Haifa Alyahya et al. This is an open access article distributed under the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.Introduction The widespread use of the Arabic language and the vast number of Arabic books and documents in several Arab and international countries has promoted researchers' interest in handwritten characters recognition [1]. In particular, these efforts were mainly intended to digitalize a large number of historical Arabic documents and ease the exploitation of the knowledge they enclose. The Arabic Handwritten Character Recognition (AHCR) problem has been formulated as a typical pattern recognition problem. Specifically, as depicted in Figure 1, the earliest AHCR systems include components shared by various image-based pattern recognition applications such as medical image classification, cancer detection, biometric, etc. The main purpose of the pre-processing phase is to eliminate the effectiveness of noise, writing size, style differences, and duplicated points [2]. *Author for correspondence The five main steps of pre-processing are size normalization, de-hooking or interpolating missing points, smoothing, slant correction or duplicated point removal, and resampling of points, as shown in Figure 2. Figure 1 Main steps of a typical character recognition systems Input Pre-processing Segmentation Feature extraction Recognition Output Research Article Abstract In recent years, handwritten character recognition has become an active research field. In particular, digitalization has triggered the interest of researchers from various computing disciplines to address several handwriting related challenges. Despite these efforts, there are still opportunities for the development and improvement of the recognition of the handwritten Arabic letters. In this paper, we designed and developed a deep ensemble architecture in which ResNet- 18 architecture is exploited to model and classify character images. Specifically, we adapted ResNet-18 by adding a dropout layer after all convolutional layer and integrated it in multiple ensemble models to automatically recognize isolated handwritten Arabic characters. A standard Arabic Handwritten Character Dataset (AHCD) was used in the experiments to train and assess all the proposed models. Satisfactory results were obtained using all models. The best- attained accuracy was 98.30% using a typical ResNet-18 model. Similarly, 98.00% and 98.03% accuracies were obtained using an ensemble model with one fully connected layer (1 FC) and an ensemble with two fully connected layers (2 FC) coupled with a dropout layer, respectively. Keywords Handwriting character recognition, Arabic, OCR, Online recognition, Handwritten recognition.