Caption-guided patent image segmentation (original) (raw)
Related papers
Figure detection and part label extraction from patent drawing images
The US Patent and Trademark Office, together with the NASA Tournament Lab, launched a contest to develop specialized algorithms to help bring the seven million patents presently in the patent archive into the digital age. The contest was hosted by TopCoder.com, the largest competitive online software developer community. The challenge was to detect, segment and recognize figures, captions and part labels from patent drawing images. The solution presented in this work was the winning submission.
2014
We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO). The goal was to detect figures and part labels in U.S. patent drawing pages. The challenge drew 232 teams of two, of which 70 teams (30%) submitted solutions. Collectively, teams submitted 1,797 solutions that were compiled on the competition servers. Participants reported spending an average of 63 hours developing their solutions, resulting in a total of 5,591 hours of development time. A manually labeled dataset of 306 patents was used for training, online system tests, and evaluation. The design and performance of the top-5 systems are presented, along with a system developed after the competition which illustrates that winning teams produced near state-of-the-art results under strict time and computation constraints. For the 1st place system, the harmonic mean of recall and precision (f-measure) was 88.57% for figure region detection, 78.81% for figure regions with correctly recognized figure titles, and 70.98% for part label detection and character recognition. Data and software from the competition are available through the online UCI Machine Learning repository to inspire follow-on work by the image processing community.
International Journal on Document Analysis and Recognition (IJDAR), 2016
We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO). The goal was to detect figures and part labels in U.S. patent drawing pages. The challenge drew 232 teams of two, of which 70 teams (30%) submitted solutions. Collectively, teams submitted 1,797 solutions that were compiled on the competition servers. Participants reported spending an average of 63 hours developing their solutions, resulting in a total of 5,591 hours of development time. A manually labeled dataset of 306 patents was used for training, online system tests, and evaluation. The design and performance of the top-5 systems are presented, along with a system developed after the competition which illustrates that winning teams produced near state-of-the-art results under strict time and computation constraints. For the 1st place system, the harmonic mean of recall and precision (f-measure) was 88.57% for figure region detection, 78.81% for figure regions with correctly recognized figure titles, and 70.98% for part label detection and character recognition. Data and software from the competition are available through the online UCI Machine Learning repository to inspire follow-on work by the image processing community.
2013
To verify the originality of an invention in a patent, the graphical description available in the form of patent drawings often plays a critical role. This paper introduces the importance, requirements and challenges of a patent image retrieval system. We present a brief account of the work done in the specific and related areas of the patent image domain. We begin with a review of work done dealing specifically with retrieval and analysis of images in the patent domain. Although the literature found dealing with patent images is small, there is a significant amount of work that has been done in related areas that is useful and applicable to the patent image area. From a methodological point of view, we present an overview of the algorithms developed for the retrieval and analysis of CAD and technical drawings, diagrams, data flow diagrams, circuit diagrams, data charts, flow charts, plots and symbol recognition.
2016
Abstract—We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO). The goal was to detect figures and part labels in U.S. patent drawing pages. The challenge drew 232 teams of two, of which 70 teams (30%) submitted solutions. Collectively, teams submitted 1,797 solutions that were compiled on the competition servers. Participants reported spending an average of 63 hours developing their solutions, resulting in a total of 5,591 hours of development time. A manually labeled dataset of 306 patents was used for training, online system tests, and evaluation. The design and performance of the top-5 systems are presented, along with a system developed after the competition which illustrates that winning teams produced near state-of-the-art results under strict time and computation constraints. For the 1st place system, the har...
Image search in patents: a review
International Journal on Document Analysis and Recognition (IJDAR), 2012
To verify the originality of an invention in a patent, the graphical description available in the form of patent drawings often plays a critical role. This paper introduces the importance, requirements and challenges of a patent image retrieval system. We present a brief account of the work done in the specific and related areas of the patent image domain. We begin with a review of work done dealing specifically with retrieval and analysis of images in the patent domain. Although the literature found dealing with patent images is small, there is a significant amount of work that has been done in related areas that is useful and applicable to the patent image area. From a methodological point of view, we present an overview of the algorithms developed for the retrieval and analysis of CAD and technical drawings, diagrams, data flow diagrams, circuit diagrams, data charts, flow charts, plots and symbol recognition.
Visual Structure Analysis of Flow Charts in Patent Images
This report presents the work carried out for the flow chart recognition task in the course of the CLEF-IP 2012 competition. The goal is to obtain structural information of flow charts based on the visual content of the images. To this end, for each flow chart a list of its nodes and their interconnections, i.e. its edges, is extracted and the type of the nodes and edges and attached text is recognized. The automatic recognition task is done in three stages: (1) flow chart image pre-processing using connected component analysis, morphological filters and line segmentation, (2) identification of nodes, junction points, end points and edges and (3) recognition of text, geometric node types and edge directions. Examples demonstrate good recognition results obtained for 100 tested flow chart images.
Towards content-based patent image retrieval: A framework perspective
World Patent Information, 2010
In this article, we discuss the potential benefits, the requirements and the challenges involved in patent image retrieval and subsequently, we propose a framework that encompasses advanced image analysis and indexing techniques to address the need for content-based patent image search and retrieval. The proposed framework involves the application of document image pre-processing, image feature and textual metadata extraction in order to support effectively content-based image retrieval in the patent domain. To evaluate the capabilities of our proposal, we implemented a patent image search engine. Results based on a series of interaction modes, comparison with existing systems and a quantitative evaluation of our engine provide evidence that image processing and indexing technologies are currently sufficiently mature to be integrated in real-world patent retrieval applications.
Patent claim segmentation for its readability
EACL 2014, 2014
Good readability of text is important to ensure efficiency in communication and eliminate risks of misunderstanding. Patent claims are an example of text whose readability is often poor. In this paper, we aim to improve claim readability by a clearer presentation. This segments the original claim text first to components of the preamble, transition, and body text and then the components further to clauses. An alternative approach would have been to modify the claim content which is, how- ever, prone to also changing the mean- ing of this legal text. Our rule-based method detects the beginning and end of the preamble (transition) [body text] with the accuracy of 100% and 97% (94% & 100%) [100% & 100%], respectively. In clause segmentation, our conditional ran- dom field (punctuation and keyword-based baseline) has the precision of 77% (41%) and recall of 76% (29%). The most com- mon reasons for segmentation errors are ambiguous coordinating conjunctions and consecutive segmentation keywords. The results give evidence for the feasibility of automated claim and clause segmentation, which may help not only inventors, re- searchers, and other laypeople to under- stand patents but also patent experts to avoid future legal cost due to litigations.
Retrieval System for Patent Images
Procedia Technology, 2013
Patent information and images play important roles to describe the novelty of an invention. However, current patent collections do not support image retrieval and patent images are become almost unsearchable. This paper presents a short review of the existing research work and challenges in patent image retrieval domain. From the review, the image feature extraction step is found to be an important step to match the query and database images successfully. In order to improve the current feature extraction step in image patent retrieval, we propose a patent image retrieval approach based on Affine-SIFT technique. Comparison discussions between the existing feature extraction techniques are presented to assess the potential of this proposed approach.