Abstract: The disclosure herein describes providing signature data of an input document. Text data of the input document is obtained (e.g., OCR data generated from image data) and a first set of signature fields are identified using signature key-value pairs of the text data. A first subset of signed signature fields and a first subset of unsigned signature fields are determined based on mapping to a set of predicted values. A second set of signature fields are determined using a region prediction model applied to image data of the input document. Region images associated with the first subset of unsigned signature fields and with second set of signature fields are obtained and a second set of signed signature fields and a second set of unsigned signature fields are determined using a signature recognition model. Signature output data is provided including signed signature fields and/or unsigned signature fields.

Abstract: Most of large-scale image retrieval systems are based on Bag-of-Visual-Words model. However, traditional Bag-of-Visual-Words model does not well capture the geometric context among local features in images, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing global geometric verification methods are either computationally expensive to ensure real-time response. To solve the above problems, a novel geometric coding algorithm is used to encode the spatial context among local features for large scale partial duplicate image retrieval. With geometric square coding and geometric fan coding, our geometric coding scheme encodes the spatial relationships of local features into three geo-maps, which are used for global verification to remove spatially inconsistent matches.

Abstract: Most of large-scale image retrieval systems are based on Bag-of-Visual-Words model. However, traditional Bag-of-Visual-Words model does not well capture the geometric context among local features in images, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing global geometric verification methods are either computationally expensive to ensure real-time response. To solve the above problems, a novel geometric coding algorithm is used to encode the spatial context among local features for large scale partial duplicate image retrieval. With geometric square coding and geometric fan coding, our geometric coding scheme encodes the spatial relationships of local features into three geo-maps, which are used for global verification to remove spatially inconsistent matches.

Abstract: A system, a computer readable storage medium including instructions, and method for generating genre models used to identify genres of a document. For each document image in a set of document images that are associated with one or more genres, the document image is segmented into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable, and features of the document image and the plurality of tiles are computed. At least one genre classifier is trained to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

Abstract: A system, a computer readable storage medium including instructions, and method for generating genre models used to identify genres of a document. For each document image in a set of document images that are associated with one or more genres, the document image is segmented into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable, and features of the document image and the plurality of tiles are computed. At least one genre classifier is trained to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.