# 2D Self-Organized ONN Model For Handwritten Text Recognition

arXiv: https://arxiv.org/abs/2207.08139 · HTML: https://mlsanity.com/paper/2207.08139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition](https://mlsanity.com/paper/1903.07377/) (2019)
- [Dropout improves Recurrent Neural Networks for Handwriting Recognition](https://mlsanity.com/paper/1312.4569/) (2013)
- [Self-Organized Operational Neural Networks with Generative Neurons](https://mlsanity.com/paper/2004.11778/) (2020)
- [Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1812.11894/) (2018)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [An Efficient End-to-End Neural Model for Handwritten Text Recognition](https://mlsanity.com/paper/1807.07965/) (2018)
- [Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers](https://mlsanity.com/paper/2105.07402/) (2021)
- [Heterogeneous Multilayer Generalized Operational Perceptron](https://mlsanity.com/paper/1804.05093/) (2018)
- [Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks](https://mlsanity.com/paper/2110.02215/) (2021)
- [Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning](https://mlsanity.com/paper/2012.14459/) (2020)
- [Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks](https://mlsanity.com/paper/2110.02381/) (2021)
- [Tensor-Based Classifiers for Hyperspectral Data Analysis](https://mlsanity.com/paper/1709.08164/) (2017)
- [Operational vs Convolutional Neural Networks for Image Denoising](https://mlsanity.com/paper/2009.00612/) (2020)
- [Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer](https://mlsanity.com/paper/2011.11436/) (2020)

## Built on by (most cited first, 2 of 2)

- [Attention Guidance Mechanism for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2403.01756/) (2024)
- [Exploring In-Context Learning for Handwritten Text Recognition](https://mlsanity.com/paper/2609.37195/) (2026)

## Abstract (arXiv)

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.