This research is the second phase in a series of investigations on developing
an Optical Character Recognition (OCR) of Arabic historical documents and
examining how different modeling procedures interact with the problem. The
first research studied the effect of Transformers on our custom-built Arabic
dataset. One of the downsides of the first research was the size of the
training data, a mere 15000 images from our 30 million images, due to lack of
resources. Also, we add an image enhancement layer, time and space
optimization, and Post-Correction layer to aid the model in predicting the
correct word for the correct context. Notably, we propose an end-to-end text
recognition approach using Vision Transformers as an encoder, namely BEIT, and
vanilla Transformer as a decoder, eliminating CNNs for feature extraction and
reducing the model's complexity. The experiments show that our end-to-end model
outperforms Convolutions Backbones. The model attained a CER of 4.46%.

本文介绍了针对阿拉伯历史文献的光学字符识别技术的研究，提出了一种端到端文本识别方法 BEIT，并通过实验比较证明，该方法优于卷积神经网络的特征提取方法，达到了 4.46% 的识别准确率。

一种基于 Transformer 模型的端到端 OCR 框架，用于识别具有变音符的大规模古典阿拉伯文多字体语料库的手写体 Arabic 识别

An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition using a Novel Transformers-based Model and an Innovative 270 Million-Words Multi-Font Corpus of Classical Arabic with Diacritics