Abstract

This paper presents an architecture that enables the recognizer to learn incrementally and, thereby adapt to document image collections for performance improvement. We argue that the recognition scheme for a book could be considerably different from that designed for isolated pages. We employ learning procedures to capture the relevant information available online, and feed it back to update the knowledge of the system. Experimental results show the effectiveness of our design for improving the performance on-the-fly.

Abstract

This paper presents an efficient indexing and retrieval scheme for searching in document image databases. In many non-European languages, optical character recognizers are not very accurate. Word spotting - word image matching - may instead be used to retrieve word images in response to a word image query. The approaches used for word spotting so far, dynamic time warping and/or nearest neighbor search, tend to be slow. Here indexing is done using locality sensitive hashing (LSH) - a technique which computes multiple hashes - using word image features computed at word level. Efficiency and scalability is achieved by content-sensitive hashing implemented through approximate nearest neighbor computation. We demonstrate that the technique achieves high precision and recall (in the 90% range), using a large image corpus consisting of seven Kalidasa’s (a well known Indian poet of antiquity) books in the Telugu language. The accuracy is comparable to using dynamic time warping and nearest neighbor search while the speed is orders of magnitude better - 20000 word images can be searched in milliseconds.

Abstract

Software engineering (SE) educators have been advocating the use of non-conventional approaches for SE education since long. In this context, we conducted action-research to compare the effectiveness of a case study approach with conventional lecture based approach. We designed and taught a first course in SE, that involved case study approach as well as the traditional lecture based approach. We recorded and analyzed student's perception of learning over using well defined parameters that reconciled with cognitive, skills and metacognitive goals of SE education. Results corroborated that case study approach is more effective and interesting for learning SE than the lecture based approach. These results indicate that academia and industry should further explore learning-by-doing paradigm, specially the case studies. Benefits of approach include bridging of the industry- academia gap and creation of professionals who are well versed with theory and practice and have experienced the intricacies of real software development even before entering the industry. This paper provides empirical data to support that case study approach is very effective in SE education and hence useful for curriculum designers. This work is useful for SE educators and researchers as it describes methodology for rigorous scientific educational researc

Abstract

This paper explores the possibility of using convex optimization to address a class of problems in visual servoing. This work is motivated by the recent success of convex optimization methods in solving geometric inference problems in computer vision. We formulate the visual servoing problem with feature visibility constraints as a convex optimization of a function of the camera position i.e. the translation of the camera. First, the path is planned using potential field method that produces unconstrained but straight line path from the initial to the desired camera position. The problem is then converted to a constrained convex optimization problem by introducing the visibility constraints to the minimization problem. The objective of the minimization process is to find for each camera position the closest alternate position from which all features are visible. This algorithm ensures that the solution is optimal. This formulation allows the introduction of more constraints, like joint limits of the arm, into the visual servoing process. The results have been illustrated in a simulation framework.

Abstract

In this paper, we have considered a real world information synthesis task, generation of a fixed length multi document summary which satisfies a specific information need. This task was mapped to a topic-oriented, informative multi-document summarization. We also tried to estimate, given the human written reference summaries and the document set, the maximum performance (ROUGE1 scores) that can be achieved by an extraction-based summarization technique. Motivated by the observation that the current approaches are far behind the estimated maximum performance, we have looked at Information Retrieval techniques to improve the relevance scoring of sentences towards information need. Following information theoretic approach we have identified a measure to capture the notion of importance or prior of a sentence. Following a different decomposition of Probability Ranking Principle, the calculated importance/prior is incorporated into the final sentence scoring by weighted linear combination. In order to evaluate the performance, we have explored information sources like WWW and encyclopedia in computing the information measure in a set of different experiments. The t-test analysis of the improvement on DUC2 2005 data set is found to be significant (p∼ 0.05). The same system has outperformed rest of the systems at DUC 2006 challenge in terms of ROUGE scores with a significant margin over the next best system.

Abstract

A large annotated corpus is critical to the development of robust optical character recognizers (OCRs). However, creation of annotated corpora is a tedious task. It is laborious, especially when the annotation is at the character level. In this paper, we propose an efficient hierarchical approach for annotation of large collection of printed document images. We align document images with independently keyed-in text. The method is model-driven and is intended to annotate large collection of documents, scanned in three different resolutions, at character level. We employ an XML representation for storage of the annotation information. APIs are provided for access at content level for easy use in training and evaluation of OCRs and other document understanding tasks.

Abstract

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that reports the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we employ a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer

Abstract

Search engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. Re-ranking of the results to reflect the most relevant results to the user using the relevance feedback has received wide attention in information retrieval in recent years. Also, sharing of information among users having similar interests using collaborative filtering techniques has achieved wide success in recommendation systems. In this paper, we propose a novel approach for re-ranking of the search results using collaborative filtering techniques using relevance feedback of a given user as well as the other users. Our approach is to learn the profiles of the users using machine learning techniques making use of past browsing histories including queries posed and documents found relevant or irrelevant. Re-ranking of the results is done using collaborative filtering techniques. First, the context of the query is inferred from the query category. The user’s community is determined dynamically in the context of the query by using the user profiles. The rank of a document is calculated using the user’s profile as well profiles of the other users in the community

Abstract

Automatic annotation is an elegant alternative to explicit recognition in images. In annotation, the image is matched with keyword models, and the most relevant keywords are assigned to the image. Using existing techniques, the annotation time for large collections is very high, while the annotation performance degrades with increase in number of keywords. Towards the goal of large scale annotation, we present an approach called “Reverse Annotation”. Unlike traditional annotationwhere keywords are identified for a given image, in Reverse Annotation, the relevant images are identified for each keyword. With this seemingly simple shift in perspective, the annotation time is reduced significantly. To be able to rank relevant images, the approach is extended to Probabilistic Reverse Annotation. Our framework is applicable to a wide variety of multimedia documents, and scalable to large collections. Here, we demonstrate the framework over a large collection of 75,000 document images, containing 21 million word segments, annotated by 35000 keywords. Our image retrieval system replicates text-based search engines, in response time.

Abstract

An indexing model is the heart of an Information Retrieval (IR) system. Data structures such as term based inverted indices have proved to be very effective for IR using vector space retrieval models. However, when functional aspects of such models were tested, it was soon felt that better relevance models were required to more accurately compute the relevance of a document towards a query. It was shown that language modeling approaches [1] in monolingual IR tasks improve the quality of search results in comparison with TFIDF [2] algorithm. The disadvantage of language modeling approaches when used in monolingual IR task as suggested in [1] is that they would require both the inverted index (term-todocument) and the forward index (document-to-term) to be able to compute the rank of document for a given query. This calls for an additional space and computation overhead when compared to inverted index models. Such a cost may be acceptable if the quality of search results are significantly improved. In a Cross-lingual IR (CLIR) task, we have previously shown in [3] that using a bilingual dictionary along with term co-occurrence statistics and language modeling approach helps improve the functional IR performance. However, no studies exist on the performance overhead in a CLIR task due to language modeling. In this paper we present an augmented index model which can be used for fast retrieval while having the benefits of language modeling in a CLIR task. The model is capable of retrieval and ranking with or without query expansion techniques using term collocation statistics of the indexed corpus. Finally we conduct performance related experiments on our indexing model to determine the cost overheads on space and time