  Deep Convolutional Neural Networks (CNNs) have recently reached
state-of-the-art Handwritten Text Recognition (HTR) performance. However,
recent research has shown that typical CNNs' learning performance is limited
since they are homogeneous networks with a simple (linear) neuron model. With
their heterogeneous network structure incorporating non-linear neurons,
Operational Neural Networks (ONNs) have recently been proposed to address this
drawback. Self-ONNs are self-organized variations of ONNs with the generative
neuron model that can generate any non-linear function using the Taylor
approximation. In this study, in order to improve the state-of-the-art
performance level in HTR, the 2D Self-organized ONNs (Self-ONNs) in the core of
a novel network model are proposed. Moreover, deformable convolutions, which
have recently been demonstrated to tackle variations in the writing styles
better, are utilized in this study. The results over the IAM English dataset
and HADARA80P Arabic dataset show that the proposed model with the operational
layers of Self-ONNs significantly improves Character Error Rate (CER) and Word
Error Rate (WER). Compared with its counterpart CNNs, Self-ONNs reduce CER and
WER by 1.2% and 3.4 % in the HADARA80P and 0.199% and 1.244% in the IAM
dataset. The results over the benchmark IAM demonstrate that the proposed model
with the operational layers of Self-ONNs outperforms recent deep CNN models by
a significant margin while the use of Self-ONNs with deformable convolutions
demonstrates exceptional results.
