UniRec-0.1B：具有1亿参数的统一文本和公式识别
UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters
AI总结：
研究旨在实现文本和公式的统一识别，提出含1亿参数的UniRec-0.1B模型。通过构建大规模数据集，应对层次结构变异性和语义纠缠等挑战，引入分层监督训练和语义解耦分词器。实验证明该模型优于同类，且速度大幅提升，有效且高效。
摘要：
文本和公式是许多文档的核心信息组成部分。准确高效地识别它们对开发强大且通用的文档解析系统至关重要。近期视觉语言模型在文本和公式统一识别上成果显著，但存在尺寸大、计算要求高的问题。本文提出仅含1亿参数的统一识别模型UniRec-0.1B，它能在字符、单词、行、段落和文档等多个层次执行文本和公式识别。为此先建立含4000万文本、公式和混合样本的大规模数据集UniRec40M以训练强大且轻量级的模型。接着识别构建此类轻量级统一专家模型的两个挑战，即层次结构变异性和文本与公式内容语义纠缠，分别引入分层监督训练和语义解耦分词器应对。最后开发涵盖多领域、多层次中英文文档的综合评估基准。实验结果表明UniRec-0.1B优于通用视觉语言模型和领先的文档解析专家模型，且速度提升2至9倍，验证了其有效性和高效性。
英文摘要：
Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable document parsing systems. Recently, vision-language models (VLMs) have achieved impressive unified recognition of text and formulas. However, they are large-sized and computationally demanding, restricting their usage in many applications. In this paper, we propose UniRec-0.1B, a unified recognition model with only 0.1B parameters. It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents. To implement this task, we first establish UniRec40M, a large-scale dataset comprises 40 million text, formula and mixed samples, enabling the training of a powerful yet lightweight model. Secondly, we identify two challenges when building such a lightweight but unified expert model. They are: structural variability across levels and semantic entanglement between textual and formulaic content. To tackle these, we introduce a hierarchical supervision training that explicitly guides structural comprehension, and a semantic-decoupled tokenizer that separates text and formula representations. Finally, we develop a comprehensive evaluation benchmark covering Chinese and English documents from multiple domains and with multiple levels. Experimental results on this and public benchmarks demonstrate that UniRec-0.1B outperforms both general-purpose VLMs and leading document parsing expert models, while achieving 2-9x speedup, validating its effectiveness and efficiency. Codebase and Dataset: https://github.com/Topdu/OpenOCR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
