DeepPatent: Large scale patent drawing recognition and retrieval

Abstract

We tackle the problem of analyzing and retrieving technical drawings. First, we introduce DeepPatent, a new largescale dataset for recognition and retrieval of design patent drawings. The dataset provides more than 350,000 design patent drawings for the purpose of image retrieval. Unlike existing datasets, DeepPatent provides fine-grained image retrieval associations within the collection of drawings and does not rely on cross-domain associations for supervision. We develop a baseline deep learning model, named Patent-Net, based on best practices for training retrieval models for static images. We demonstrate the superior performance of PatentNet when trained on our fine-grained associations of DeepPatent against other deep learning approaches and classic computer vision descriptors. With the introduction of this new dataset, and benchmark algorithms, we demonstrate that the analysis and retrieval of technical drawings remains an open challenge in computer vision; and that patent drawing retrieval provides a real-world testbench to spur research.

Reliable Multi-View Deep Patent Classification

“…Due to the inherent properties of the patent, textual information is often the most widely exploited factor that distinguishes the different types of patents. Therefore, the automatic deep patent classification method always builds a deep neural network, such as a convolutional neural network (CNN) [19,20], residual network (ResNet) [21], BERT [22], graph neural network (GNN) [23,24], and so on, to extract and classify the textual information. For example, PatentBERT [22] utilizes the pre-training language model BERT to extract the features and classify them into the corresponding classes.…”