  This study explores the transfer learning capabilities of the TrOCR
architecture to Spanish. TrOCR is a transformer-based Optical Character
Recognition (OCR) model renowned for its state-of-the-art performance in
English benchmarks. Inspired by Li et al. assertion regarding its adaptability
to multilingual text recognition, we investigate two distinct approaches to
adapt the model to a new language: integrating an English TrOCR encoder with a
language specific decoder and train the model on this specific language, and
fine-tuning the English base TrOCR model on a new language data. Due to the
scarcity of publicly available datasets, we present a resource-efficient
pipeline for creating OCR datasets in any language, along with a comprehensive
benchmark of the different image generation methods employed with a focus on
Visual Rich Documents (VRDs). Additionally, we offer a comparative analysis of
the two approaches for the Spanish language, demonstrating that fine-tuning the
English TrOCR on Spanish yields superior recognition than the language specific
decoder for a fixed dataset size. We evaluate our model employing character and
word error rate metrics on a public available printed dataset, comparing the
performance against other open-source and cloud OCR spanish models. As far as
we know, these resources represent the best open-source model for OCR in
Spanish. The Spanish TrOCR models are publicly available on HuggingFace [20]
and the code to generate the dataset is available on Github [25].
