CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
Practical algorithms for image analysis: description, examples, and code
Practical algorithms for image analysis: description, examples, and code
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Image processing by simulated annealing
IBM Journal of Research and Development - High-density magnetic recording
Automatic categorization of figures in scientific documents
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Automatic Extraction of Data from 2-D Plots in Documents
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Context-based multiscale classification of document images using wavelet coefficient distributions
IEEE Transactions on Image Processing
Automatic extraction of data points and text blocks from 2-dimensional plots in digital documents
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Patent image retrieval: a survey
Proceedings of the 4th workshop on Patent information retrieval
Hi-index | 0.00 |
Most search engines index the textual content of documents in digital libraries. However, scholarly articles frequently report important findings in figures for visual impact and the contents of these figures are not indexed. These contents are often invaluable to the researcher in various fields, for the purposes of direct comparison with their own work. Therefore, searching for figures and extracting figure data are important problems. To the best of our knowledge, there exists no tool to automatically extract data from figures in digital documents. If we can extract data from these images automatically and store them in a database, an end-user can query and combine data from multiple digital documents simultaneously and efficiently. We propose a framework based on image analysis and machine learning to extract information from 2-D plot images and store them in a database. The proposed algorithm identifies a 2-D plot and extracts the axis labels, legend and the data points from the 2-D plot. We also segregate overlapping shapes that correspond to different data points. We demonstrate performance of individual algorithms, using a combination of generated and real-life images.