The subject about document image understanding is to extract and classify individual data meaningfully from paper-based documents. Until today, many methods/approaches have been proposed with regard to recognition of various kinds of documents, various technical problems for extensions of OCR, and requirements for practical usages. Of course, though the technical research issues in the early stage are looked upon as complementary attacks for the traditional OCR which is dependent on character recognition techniques, the application ranges or related issues are widely investigated or should be established progressively. This paper addresses current topics about document image understanding from a technical point of view as a survey. key words: document model, top-down, bottom-up, layout structure, logical structure, document types, layout recognition

Document Analysis and Recognition

Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

We describe a method for automatically obtaining object representations suitable for retrieval from generic video shots. The object representation consists of an association of frame regions. These regions provide exemplars of the object's possible visual appearances.

Two ideas are developed: (i) associating regions within a single shot to represent a deforming object; (ii) associating regions from the multiple visual aspects of a 3D object, thereby implicitly representing 3D structure. For the association we exploit temporal continuity (tracking) and wide baseline matching of affine covariant regions.

In the implementation there are three areas of novelty: First, we describe a method to repair short gaps in tracks. Second, we show how to join tracks across occlusions (where many tracks terminate simultaneously). Third, we develop an affine factorization method that copes with motion degeneracy.

We obtain tracks that last throughout the shot, without requiring a 3D reconstruction. The factorization method is used to associate tracks into object-level groups, with common motion. The outcome is that separate parts of an object that are not simultaneously visible (such as the front and back of a car, or the front and side of a face) are associated together. In turn this enables object-level matching and recognition throughout a video.

We illustrate the method on the feature film "Groundhog Day." Examples are given for the retrieval of deforming objects (heads, walking people) and rigid objects (vehicles, locations).

/pdf/object-level-grouping-for-video-shots-1vsfqifb5l.pdf

Object Level Grouping for Video Shots

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Offline Recognition of Devanagari Script: A Survey

Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researchers have proposed numerous techniques for table detection based on layout analysis of documents. Most of these techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. In this paper, we have presented a deep learning based method for table detection. In the proposed method, document images are first pre-processed. These images are then fed to a Region Proposal Network followed by a fully connected neural network for table detection. The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines. We have done our evaluations on publicly available UNLV dataset where it beats Tesseract's state of the art table detection system by a significant margin.

Table Detection Using Deep Learning

This paper presents an XML-based scheme for managing a large multilingual OCR project. In particular we describe how a new XML based tagging scheme has been exploited to achieve the objectives of the project. Managing a large multi-lingual OCR project involving multiple research groups, developing script specific and script independent technologies in a collaborative fashion is a challenging problem. In this paper, we present some of the software and data management strategies designed for the project aimed at developing OCR for 11 scripts of Indian origin for which mature OCR technology was not available.

Managing multilingual OCR project using XML

Real world applicability of many computer vision solutions is constrained by the mismatch between the training and test domains. This mismatch might arise because of factors such as change in pose, lighting conditions, quality of imaging devices, intra-class variations inherent in object categories etc. In this work, we present a dictionary learning based approach to tackle the problem of domain mismatch. In our approach, we jointly learn dictionaries for the source and the target domains. The dictionaries are partially shared, i.e. some elements are common across both the dictionaries. These shared elements can represent the information which is common across both the domains. The dictionaries also have some elements to represent the domain specific information. Using these dictionaries, we separate the domain specific information and the information which is common across the domains. We use the latter for training cross-domain classifiers i.e., we build classifiers that work well on a new target domain while using labeled examples only in the source domain. We conduct cross-domain object recognition experiments on popular benchmark datasets and show improvement in results over the existing state of art domain adaptation approaches.

/pdf/learning-partially-shared-dictionaries-for-domain-adaptation-3ck2wj77t0.pdf

Learning Partially Shared Dictionaries for Domain Adaptation

This paper investigates the problem of cross document image retrieval, ie use of query images from one style (say font) to perform retrieval from a collection which is in a different style (say a different set of books) We present two approaches to tackle this problem We propose an effective style independent retrieval scheme using a nonlinear style-content separation model We also propose a semi-supervised style transfer strategy to expand the query into multiple styles We validate both these approaches on a collection of word images which vary in fonts/styles

/pdf/enhancing-word-image-retrieval-in-presence-of-font-27t453unt9.pdf

Enhancing Word Image Retrieval in Presence of Font Variations

We present a novel scheme for object-based video sequence presentation using appearance spaces. Our scheme enables fully automatic extraction of semantic video objects for a class of sequences, and their supervised organization in an object-class hierarchy. The hierarchy can be used for generic classification of query video objects, and transcoding using semantics of video objects.

A framework for video representation and transcoding using appearance spaces

The mismatch between the training data and the test data distributions is a challenging issue while designing many practical computer vision systems. In this paper, we propose a domain adaptation technique to tackle this issue. We are interested in a domain adaptation scenario where source domain has large amount of labeled examples and the target domain has large amount of unlabeled examples. We align the source domain subspace with the target domain subspace in order to reduce the mismatch between the two distributions. We model the subspace using Locality Preserving Projections (LPP). Unlike previous subspace alignment approaches, we introduce a strategy to effectively utilize the training labels in order to learn discriminative subspaces. We validate our domain adaptation approach by testing it on two different domains, i.e. handwritten and printed digit images. We compare our approach with other existing approaches and show the superiority of our method.

https://cvit.iiit.ac.in/images/ConferencePapers/2015/Viresh2015Domain.pdf

Gaurav Harit

Papers

Managing multilingual OCR project using XML

Learning Partially Shared Dictionaries for Domain Adaptation

Enhancing Word Image Retrieval in Presence of Font Variations

A framework for video representation and transcoding using appearance spaces

Domain adaptation by aligning locality preserving subspaces