  The laws of model size, data volume, computation and model performance have
been extensively studied in the field of Natural Language Processing (NLP).
However, the scaling laws in Optical Character Recognition (OCR) have not yet
been investigated. To address this, we conducted comprehensive studies that
involved examining the correlation between performance and the scale of models,
data volume and computation in the field of text recognition.Conclusively, the
study demonstrates smooth power laws between performance and model size, as
well as training data volume, when other influencing factors are held constant.
Additionally, we have constructed a large-scale dataset called REBU-Syn, which
comprises 6 million real samples and 18 million synthetic samples. Based on our
scaling law and new dataset, we have successfully trained a scene text
recognition model, achieving a new state-ofthe-art on 6 common test benchmarks
with a top-1 average accuracy of 97.42%. The models and dataset are publicly
available at https://github.com/large-ocr-model/large-ocr-model.github.io.
