Abstract
Conventional optical character recognition (OCR) techniques segmented each character and then recognized. This made them prone to error in character segmentation, and devoid of context to exploit language models. Advances in sequence to sequence translation in last decade led to modern techniques first detecting words and then inputting one word at a time to a model to directly output full words as sequence of characters. This allowed better utilization of language models and bypass error-prone character segmentation step. We observe that the above transition in style has moved the bottleneck in accuracy to word segmentation. Hence, in this paper, we propose a natural and logical progression from word level OCR to line-level OCR. The proposal allows to bypass errors in word detection, and provides larger sentence context for better utilization of language models. We show that the proposed technique not only improves the accuracy but also efficiency of OCR. Despite our thorough literature survey, we did not find any public dataset to train and benchmark such shift from word to line-level OCR. Hence, we also contribute a meticulously curated dataset of 251 English page images with line-level annotations. Our experimentation revealed a notable end-to-end accuracy improvement of 5.4%, underscoring the potential benefits of transitioning towards line-level OCR, especially for document images. We also report a 4 times improvement in efficiency compared to word-based pipelines. With continuous improvements in large language models, our methodology also holds potential to exploit such advances. Project Website: this https URL
Abstract (translated)
传统的光学字符识别（OCR）技术是先对每个字符进行分割，然后逐一识别。这种方法在字符分割阶段容易出错，并且缺乏上下文信息来利用语言模型。近年来，在序列到序列翻译领域的进展促使现代技术首先检测单词，然后再一次输入一个单词至模型中以直接输出完整的单词字符序列。这使得更好地利用语言模型成为可能，并跳过了易出错的字符分割步骤。 我们观察到上述风格的变化将准确性瓶颈转移到了词级切分上。因此，在本文中，我们提出了从词级OCR自然且逻辑地过渡到行级OCR的方法。该方法能够规避单词检测中的错误，并提供更大的句子上下文以更好地利用语言模型。我们表明所提出的这种方法不仅提高了OCR的准确率，还提升了其效率。 尽管进行了详尽的文献调查，但我们没有找到任何可用于训练和评估从词级转向行级OCR转变的公开数据集。因此，我们也贡献了一个精心策划的数据集，包含251张英文页面图像，并附有行级别注释。我们的实验结果显示了显著的整体准确率提升达5.4%，突显了向行级OCR转换的潜在好处，特别是对文档图像而言。此外，相比基于单词的流水线方法，我们还报告了效率提高了四倍。 随着大型语言模型的持续改进，我们的方法也具有利用这些进步的潜力。项目网站：[此链接](https://example.com/)（请将示例网址替换为实际提供的项目网址）
URL
https://arxiv.org/abs/2508.21693
