Offline Arabic handwritten word recognition: A transfer learning approach

بيــانــات القــسم

الفــرع: الأقسام الهندسية
القــسم: قسم الهندسة الكهربية (شعبة هندسة الالكترونيات والاتصالات)
المــكان: مبنى قسم الهندسة الكهربية

تفــاصيــل النــشرة

أســم الدكــتور	د-محمد عونى احمد
أســم النــشرة	Offline Arabic handwritten word recognition: A transfer learning approach
وصــف النــشرة	Offline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to compensate for the lack of training samples, but there is a wide controversy about the effectiveness of applying it to cross-domain tasks. In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. Then, we evaluate the performance of the ResNet18 model that has been pre-trained on the ImageNet dataset for the same task. Finally, we propose an approach based on sequentially transferring the mid-level word image representations through two consecutive phases using the ResNet18 model.