With the continuous development of OCR technology and the expansion of
application fields, text recognition in complex scenes has become a key
challenge. Factors such as multiple fonts, mixed scenes and complex layouts
seriously affect the recognition accuracy of traditional OCR models. Although
OCR models based on deep learning have performed well in specific fields or
similar data sets in recent years, the generalization ability and robustness of
the model are still a big challenge when facing complex environments with
multiple scenes. Furthermore, training an OCR model from scratch or fine-tuning
all parameters is very demanding on computing resources and inference time,
which limits the flexibility of its application. This study focuses on a
fundamental aspect of mixed text recognition in response to the challenges
mentioned above, which involves effectively fine-tuning the pre-trained basic
OCR model to demonstrate exceptional performance across various downstream
tasks. To this end, we propose a parameter-efficient hybrid text recognition
method based on pre-trained OCR Transformer, namely DLoRA-TrOCR. This method
embeds DoRA into the image encoder and LoRA into the internal structure of the
text decoder, enabling efficient parameter fine-tuning for downstream tasks.
Experimental results show that compared to similar parameter adjustment
methods, our model DLoRA-TrOCR has the smallest number of parameters and
performs better. It can achieve state-of-the-art performance on complex scene
data sets involving simultaneous recognition of mixed handwritten, printed and
street view texts.

本研究旨在通过对预训练基础 OCR 模型进行有效的参数微调，在各种下游任务中展示出卓越的性能。我们提出了一种基于预训练 OCR Transformer 的参数高效混合文本识别方法，即 DLoRA-TrOCR。该方法将 DoRA 嵌入图像编码器和 LoRA 嵌入文本解码器的内部结构，使得下游任务的参数微调更加高效。实验结果表明，与类似的参数调整方法相比，我们的模型 DLoRA-TrOCR 具有最少的参数，并且表现更好。它可以在包括混合手写、印刷和街景文本的复杂场景数据集上取得最先进的性能。

DLoRA-TrOCR：基于 Transformer 的混合文本模式光学字符识别

DLoRA-TrOCR: Mixed Text Mode Optical Character Recognition Based On  Transformer

Reading seal title text is a challenging task due to the variable shapes of
seals, curved text, background noise, and overlapped text. However, this
important element is commonly found in official and financial scenarios, and
has not received the attention it deserves in the field of OCR technology. To
promote research in this area, we organized ICDAR 2023 competition on reading
the seal title (ReST), which included two tasks: seal title text detection
(Task 1) and end-to-end seal title recognition (Task 2). We constructed a
dataset of 10,000 real seal data, covering the most common classes of seals,
and labeled all seal title texts with text polygons and text contents. The
competition opened on 30th December, 2022 and closed on 20th March, 2023. The
competition attracted 53 participants from academia and industry including 28
submissions for Task 1 and 25 submissions for Task 2, which demonstrated
significant interest in this challenging task. In this report, we present an
overview of the competition, including the organization, challenges, and
results. We describe the dataset and tasks, and summarize the submissions and
evaluation results. The results show that significant progress has been made in
the field of seal title text reading, and we hope that this competition will
inspire further research and development in this important area of OCR
technology.

本文介绍了 ICDAR 2023 印章标题文本识别（ReST）比赛，并概述了比赛的组织、挑战和结果，报道了来自学术界和工业界的 53 位参与者，包括 28 个 Task 1 的提交和 25 个 Task 2 的提交，结果表明印章标题文本识别领域已取得了重要进展，激励了进一步的研究和发展。

ICDAR 2023 读取印章比赛

ICDAR 2023 Competition on Reading the Seal Title

Scholars in the humanities rely heavily on ancient manuscripts to study
history, religion, and socio-political structures in the past. Many efforts
have been devoted to digitizing these precious manuscripts using OCR
technology, but most manuscripts were blemished over the centuries so that an
Optical Character Recognition (OCR) program cannot be expected to capture faded
graphs and stains on pages. This work presents a neural spelling correction
model built on Google OCR-ed Tibetan Manuscripts to auto-correct OCR-ed noisy
output. This paper is divided into four sections: dataset, model architecture,
training and analysis. First, we feature-engineered our raw Tibetan etext
corpus into two sets of structured data frames -- a set of paired toy data and
a set of paired real data. Then, we implemented a Confidence Score mechanism
into the Transformer architecture to perform spelling correction tasks.
According to the Loss and Character Error Rate, our Transformer + Confidence
score mechanism architecture proves to be superior to Transformer, LSTM-2-LSTM
and GRU-2-GRU architectures. Finally, to examine the robustness of our model,
we analyzed erroneous tokens, visualized Attention and Self-Attention heatmaps
in our model.

本篇论文介绍了一种使用 OCR 技术对西藏手稿进行数字化处理并采用神经拼写纠正模型进行自动纠正噪声输出的方法， 通过 Transformer + 置信度得分机制，证明该方法比 Transformer，LSTM-2-LSTM 和 GRU-2-GRU 架构更为优越，为人文社科领域研究提供了方便。