14,816 research outputs found
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
We present an exhaustive investigation of recent Deep Learning architectures,
algorithms, and strategies for the task of document image classification to
finally reduce the error by more than half. Existing approaches, such as the
DeepDocClassifier, apply standard Convolutional Network architectures with
transfer learning from the object recognition domain. The contribution of the
paper is threefold: First, it investigates recently introduced very deep neural
network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from
real images). Second, it proposes transfer learning from a huge set of document
images, i.e. 400,000 documents. Third, it analyzes the impact of the amount of
training data (document images) and other parameters to the classification
abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP
dataset. We achieve an accuracy of 91.13% for the Tobacco-3482 dataset while
earlier approaches reach only 77.6%. Thus, a relative error reduction of more
than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90.97% is
achieved, corresponding to a relative error reduction of 11.5%
Math Search for the Masses: Multimodal Search Interfaces and Appearance-Based Retrieval
We summarize math search engines and search interfaces produced by the
Document and Pattern Recognition Lab in recent years, and in particular the min
math search interface and the Tangent search engine. Source code for both
systems are publicly available. "The Masses" refers to our emphasis on creating
systems for mathematical non-experts, who may be looking to define unfamiliar
notation, or browse documents based on the visual appearance of formulae rather
than their mathematical semantics.Comment: Paper for Invited Talk at 2015 Conference on Intelligent Computer
Mathematics (July, Washington DC
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis
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