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The scientific problem of real-time camera-based document image retrieval is achieved by computing the image features adapted to this acquisition mode i.e. the image features which are highly discriminative even under challenging conditions of camera capture as well as which are light to be computed. In this paper, we propose new extension features to our previously proposed SRIF descriptor. The new...
In this paper, we propose a new feature vector, named Scale and Rotation Invariant Features (SRIF), for real-time camera-based document image retrieval. SRIF is based on Locally Likely Arrangement Hashing (LLAH), which has been widely used and accepted as an efficient real-time camera-based document image retrieval method based on text. SRIF is computed based on geometrical constraints between pairs...
Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format. Current state-of-the-art works lack databases and baseline benchmarks for digitizing mobile captured documents. We have organized a competition for mobile document capture and OCR...
In this paper, we present camera-based document retrieval systems using various local features as well as various indexing methods. We employ our recently developed features, named Scale and Rotation Invariant Features (SRIF), which are computed based on geometrical constraints between pairs of nearest points around a keypoint. We compare SRIF with state-of-the-art local features. The experimental...
We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph...
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