An Empirical Study of Scaling Law for OCR

29 Dec 2023  ·  Miao Rang, Zhenni Bi, Chuanjian Liu, Yunhe Wang, Kai Han ·

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.

PDF Abstract

Datasets


Introduced in the Paper:

REBU-Syn

Used in the Paper:

ICDAR 2013 SVT CUTE80 SVTP

Results from the Paper


 Ranked #1 on Scene Text Recognition on ICDAR2013 (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Scene Text Recognition CUTE80 CLIP4STR-B* Accuracy 99.65 # 3
Scene Text Recognition ICDAR2013 CLIP4STR-L* Accuracy 99.42 # 1
Scene Text Recognition ICDAR2015 CLIP4STR-L* Accuracy 92.6 # 2
Scene Text Recognition SVT CLIP4STR-B* Accuracy 98.76 # 3
Scene Text Recognition SVTP CLIP4STR-L* Accuracy 98.13 # 3

Methods


No methods listed for this paper. Add relevant methods here