Textual summarisation of flowcharts in patent drawings for CLEF-IP 2012 (original) (raw)
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CVC-UAB’s participation in the Flowchart Recognition Task of CLEF-IP 2012
Abstract. The aim of this document is to describe the methods we used in the flowchart recognition task of the CLEF-IP 2012 track. The flowchart recognition task consisted in interpreting flowchart linedrawing images. The participants are asked to extract as much as structural information in these images as possible and return it in a predefined textual format for further processing for the purpose of patent search.
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.
Flowchart recognition for non-textual information retrieval in patent search
Information Retrieval, 2013
Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.
Modeling Flowchart Structure Recognition as a Max-Sum Problem
2013 12th International Conference on Document Analysis and Recognition, 2013
This work deals with the on-line recognition of hand-drawn graphical sketches with structure. We present a novel approach, in which the search for a suitable interpretation of the input is formulated as a combinatorial optimization task-the max-sum problem. The recognition pipeline consists of two main stages. First, groups of strokes possibly representing symbols of a sketch (symbol candidates) are segmented and relations between them are detected. Second, a combination of symbol candidates best fitting the input is chosen by solving the optimization problem. We focused on flowchart recognition. Training and testing of our method was done on a freely available benchmark database. We correctly segmented and recognized 82.7% of the symbols having 31.5% of the diagrams recognized without any error. It indicates that our approach has promising potential and can compete with the state-of-the-art methods.
Extraction, layout analysis and classification of diagrams in PDF documents
2003
Abstract Diagrams are a critical part of virtually all scientific and technical documents. Analyzing diagrams will be important for building comprehensive document retrieval systems. This paper focuses on the extraction and classification of diagrams from PDF documents. We study diagrams available in vector (not raster) format in online research papers.
Recognizing Off-Line Flowcharts by Reconstructing Strokes and Using On-Line Recognition Techniques
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016
We experiment with off-line recognition of handwritten flowcharts based on strokes reconstruction and our state-of-the-art on-line diagram recognizer. A simple baseline algorithm for strokes reconstruction is presented and necessary modifications of the original recognizer are identified. We achieve very promising results on a flowcharts database created as an extension of our previously published on-line database.
First experiments on a new online handwritten flowchart database
2011
We propose in this paper a new online handwritten flowchart database and perform some first experiments to have a baseline benchmark on this dataset. The collected database consists of 419 flowcharts labeled at the stroke and symbol levels. In addition, an isolated database of graphical and text symbols was extracted from these collected flowcharts. Then, we tackle the problem of online handwritten flowchart recognition from two different points of view. Firstly, we consider that flowcharts are correctly segmented, and we propose different classifiers to perform two tasks, text/non-text separation and graphical symbol recognition. Tested with the extracted isolated test database, we achieve up to 90% and 98% in text/non-text separation and up to 93.5% in graphical symbols recognition. Secondly, we propose a global approach to perform flowchart segmentation and recognition. For this latter, we adopt a global learning schema and a recognition architecture that considers a simultaneous segmentation and recognition. Global architecture is trained and tested directly with flowcharts. Results show the interest of such global approach, but regarding the complexity of flowchart segmentation problem, there is still lot of space to improve the global learning and recognition methods.
Proceedings of the 11th International Joint Conference on Artificial Intelligence Volume 1, 1989
This paper will present a generalisation of chart parsing able to cope with the case where the object being parsed is a particular kind of diagram (a flowgraph) and the grammar is an appropriate type of graph grammar (a flowgraph grammar). A feature that often occurs in such diagrams is structure sharing. This paper also discusses the problem of diagram recognition in the case where structure sharing is allowed, noting that we want to permit structure sharing, but not enforce it.
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.