A new visual signature for content-based indexing of low resolution documents

This paper proposes a new visual signature for content –based indexing of low resolution documents. Camera Based Document Analysis and Recognition (CBDAR) has been established which deals with the textual information in scene images taken by low cost hand held devices like digital camera, cell phones, etc. A lot of applications like text translation, reading text for visually impaired and blind person, information retrieval from media document, e-learning, etc., can be built using the techniques developed in CBDAR domain. The proposed approach of extraction of textual information is composed of three steps: image segmentation, text localization and extraction, and Optical Character Recognition. First of all, for pre-processing the resolution of each image is checked for re-sampling to a common resolution format (720 X 540). Then, the final image is converted to grayscale and binarized using Otsu segmentation method for further processing. In addition, looking at the mean horizontal run length of both black and white pixels, the proper segmentation of foreground objects is checked. In the post-processing step, the text localizer validates the candidate text regions proposed by text detector. We have employed a connected component approach for text localization. The extracted text is then has been successfully recognized using ABBYY FineReader for OCR. Apart from OCR, we had created a novel feature vectors from textual information for Content-Based Image Retrieval (CBIR)

Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces

This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library

Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services

Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers

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