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Fine-grained object recognition is more challenging than generic categorization due to the subtle difference between subcategories under the large intra-class pose change and appearance variations. The state-of-the-art fine-grained recognition methods usually utilize part detection or pose alignment to alleviate the pose variation, and then use convolutional neural networks (CNNs) to extract local...
Graphical objects are important elements of freely handwritten notes but their segmentation from the document is challenging due to their irregular properties. This paper introduces an original solution for automatically segmenting diagrams and drawings from unstructured online documents. We propose a multi-scale representation of the document modeled as a hierarchical Conditional Random Field to...
This paper proposes a new method for fast text localization in natural scene images by combining learning-based region filtering and verification in a coarse-to-fine strategy. In each pyramid layer, a boosted region filter is used to extract candidate text regions, which are segmented into candidate text lines by multi-orientation projection analysis. A polynomial classifier with combined features...
Annotating the regions, text lines and characters of document images is an important, but tedious and expensive task. A ground-truthing tool may largely alleviate the human burden in this process. This paper describes an automated recognition-based tool GTLC for finding the best alignment between the text transcript and the connected components of unconstrained handwritten document image. The alignment...
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