Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches. The new DECIMER model presented here refines our previous research efforts and is currently the only open-source model tailored specifically for the recognition of hand-drawn chemical structures. The enhanced model performs better in handling variations in handwriting styles, line thicknesses, and background noise, making it suitable for real-world applications. The DECIMER hand-drawn structure recognition model and its source code have been made available as an open-source package under a permissive license.

中文翻译：

---

通过增强的 DECIMER 架构在手绘化学结构识别方面取得进展


准确识别手绘化学结构对于数字化传统实验室笔记本中的手写化学信息或促进平板电脑或智能手机上基于手写笔的结构输入至关重要。然而，手绘结构固有的可变性给现有的光学化学结构识别（OCSR）软件带来了挑战。为了解决这个问题，我们提出了一种增强型化学图像识别深度学习 (DECIMER) 架构，该架构利用卷积神经网络 (CNN) 和 Transformer 的组合来提高对手绘化学结构的识别。该模型采用了 EfficientNetV2 CNN 编码器，可从手绘图像中提取特征，然后是 Transformer 解码器，可将提取的特征转换为简化分子输入行输入系统 (SMILES) 字符串。我们的模型使用 RanDepict 生成的合成手绘图像进行训练，RanDepict 是一种用不同风格元素描绘化学结构的工具。使用真实世界的手绘化学结构数据集进行基准测试，以评估模型的性能。结果表明，与其他方法相比，我们改进的 DECIMER 架构表现出显着提高的识别精度。这里介绍的新 DECIMER 模型完善了我们之前的研究工作，是目前唯一专门为识别手绘化学结构而定制的开源模型。增强的模型在处理手写风格、线条粗细和背景噪声的变化方面表现更好，使其适合现实世界的应用。 DECIMER 手绘结构识别模型及其源代码已在许可下作为开源包提供。