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通过增强的DECIMER架构,手绘化学结构识别方面的进展

Kohulan Rajan¹, Henning Otto Brinkhaus¹, Achim Zielesny²

¹Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.

Journal of cheminformatics

|July 5, 2024

概括

本研究引入了一种增强的化学图像识别深度学习 (DECIMER) 模型,用于准确识别手绘化学结构. 改进的模型在从各种手写风格中数字化化学信息方面表现出卓越的性能.

科学领域:

化学化学化学
计算机科学计算机科学
人工智能的人工智能

背景情况:

精确识别手工绘制的化学结构对于数字化旧数据和启用新输入方法至关重要.
现有的光学化学结构识别 (OCSR) 软件在与手绘结构固有的变性作斗争.

研究的目的:

开发一种增强的化学图像识别深度学习 (DECIMER) 模型,能够准确识别手绘化学结构.
通过解决手写变异带来的挑战,改进现有的OCSR方法.

主要方法:

开发了一个增强的DECIMER架构,结合了卷积神经网络 (CNN) 和变压器.
一个EfficientNetV2 CNN编码器提取特征,一个变压器解码器生成简化分子输入线输入系统 (SMILES) 字符串.
模型使用RanDepict生成的合成手绘图像进行训练,并根据真实世界的数据进行基准测试.

主要成果:

与其他方法相比,改进的DECIMER架构显示了显著提高的识别准确性.
该模型有效地处理手写风格,线条厚度和背景噪音的变化.
使用现实世界的手绘化学结构数据集来验证性能.

结论:

增强的DECIMER模型为识别手绘化学结构提供了卓越的准确性.
这种开源模型是第一个为手绘结构量身定制的模型,适用于现实世界的应用.
DECIMER模型和源代码在许可许可许可证下公开提供.

关键词:

化学结构识别化学结构识别一个分数器.深度学习是一种深度学习.手工绘制的化学结构这是OCSRR的OCSR.光学化学结构识别技术化学结构识别变压器变压器变压器

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