LAIF: AI, Deep Learning for Germany Suetterlin Letter Recognition and Generation

TL;DR

This paper introduces LAIF, a deep learning framework for recognizing and generating Germany Suetterlin handwritten letters, utilizing convolutional neural networks and generative adversarial networks to overcome data scarcity.

Contribution

The paper presents a novel deep learning framework combining CNNs and GANs specifically for Suetterlin letter recognition and generation, addressing data limitations.

Findings

Effective recognition of Suetterlin letters using CNNs.

Synthetic data generation with GANs improves model training.

Open-source code available for replication.

Abstract

One of the successful early implementation of deep learning AI technology was on letter recognition. With the recent breakthrough of artificial intelligence (AI) brings more solid technology for complex problems like handwritten letter recognition and even automatic generation of them. In this research, we proposed deep learning framework called Ludwig AI Framework(LAIF) for Germany Suetterlin letter recognition and generation. To recognize Suetterlin letter, we proposed deep convolutional neural network. Since lack of big amount of data to train for the deep models and huge cost to label existing hard copy of handwritten letters, we also introduce the methodology with deep generative adversarial network to generate handwritten letters as synthetic data. Main source code is in https://github.com/enkhtogtokh/LAIF repository.