Reverse-engineering bar charts extract textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts.

中文翻译：

---

使用神经网络逆向工程条形图

逆向工程条形图从条形图的视觉表示中提取文本和数字信息，以支持需要底层信息的应用场景。在本文中，我们提出了一种基于神经网络的逆向工程条形图方法。我们采用基于神经网络的对象检测模型来同时定位和分类文本信息。这种方法提高了文本信息提取的效率。我们设计了一个编码器-解码器框架，它集成了卷积神经网络和循环神经网络来提取数字信息。我们进一步在框架中引入了注意力机制，以实现高精度和鲁棒性。合成数据集和真实数据集用于评估该方法的有效性。据我们所知，