Enhancing Transformer-Based Language Models for Hungarian Handwritten Text Recognition.

Optical Character Recognition (OCR) is still working on making a multilingual model that incorporates the Hungarian language. We introduce a hybrid Hungarian and English model, one of the biggest challenges is to recognize handwritten text. We are going to investigate a set of models in this research, such as TrOCR large-handwritten, leveraging PULI-BERT, and Roberta-base with Diet models. The digitization of documents, and the preservation of cultural heritage specifically, has long been a research problem related to text recognition. We use an extensive text on the recognition approach using pre-trained visual and language transformer models. We pre-train the TrOCR proposed by Microsoft researchers for both large and base models at the first phase and then fine-tune them on human data at the second stage. Then, leverage new pre-trained transformers models such as Roberta-base, and PULI-BERT, as decoders and Diet, Vit, and Beit as encoder models at the pre-training phase on generated synthetic data and then fine-tune them on a small amount of human-annotated data provided by (DH-Lab) researchers with augmentation and without augmentation. Developed using tiny-scale Synthetic data of around three-million-line text open-source corpus, and subsequently refined using tiny person-labeled datasets. Experiments showed that the best CER is 3.681 in the TrOCR large handwritten, and the best WER is 16.091 by leveraging the PULI-BERT with the Deit model. These fine-tuned models outperform the currently existing state-of-the-art TrOCR models on historical Hungarian handwriting, according to the benchmark results on the János Arany dataset.