Albanian Handwritten Text Recognition using Synthetic Datasets and Pre-Trained Models

Handwritten Text Recognition (HTR) has continuously attracted the focus of researchers to enable the integration of technology into our daily lives. Handwritten text recognition (HTR), a technology of considerable importance, takes a leading role in the analysis and digitization of various documents. This technology is important in facilitating the efficient use of handwritten documents, especially within academic, historical, and cultural contexts. The use of artificial intelligence in handwriting recognition offers a very good opportunity to achieve satisfactory results in this field, but to achieve good results a large dataset is needed. Creating a large dataset to train different AI models is a challenge for languages with limited resources such as the Albanian language. This paper aims to present a novel approach to the development of an HTR system for the Albanian language using an attention-based encoder-decoder architecture. The dataset used in the experiments is a synthetic dataset generated using deep learning techniques based on the English language dataset as they are both variants of the Latin alphabet. We enhanced the dataset with two letters specific to Albanian, (“ë” and “ç”). The usage of pre-trained English models for handwriting recognition improved our model’s performance. The results of the experiments are very promising and prove that our approach is efficient in recognizing handwriting in the Albanian language. This shows that the attention-based encoder-decoder architecture can be adopted for different languages with limited resources.