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 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

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

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 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

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 the experiments of Language Technologies Research Centre (LTRC) 1 as part of their participation in CLEF2 2007 Indian language to English ad-hoc cross language document retrieval task. In this paper we discuss our Hindi and Telugu to English CLIR system and the experiments using CLEF 2007 dataset. We used a variant of TFIDF algorithm in combination with a bilingual lexicon for query translation. We also explored the role of a document summary in fielded queries and two different boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of documents. We also find that simple disjunctive combination of translated query

Abstract

A read-only transaction (ROT) does not modify any data. The main issues regarding processing ROTs are correctness, data currency and performance. Two-phase Locking (2PL) protocol is widely used for concur-rency control with serializabilty as correctness criteria. Even though 2PL processes ROTs correctly with no data currency related issues, the performance deteriorates as data contention increases. To improve the performance, an approach has been proposed in the literature by considering “Snapshot Isolation (SI)” as correctness criteria to process ROTs. At SI-level, ROTs are processed by reading from a snapshot of the committed data and ignoring the modifications produced by the concurrent active transactions. Even though SI-based algorithms improve the performance of ROTs, data currency of ROTs and correctness (serializability) are compromised. In this paper, we propose an approach by extending the notion of speculation to improve the performance of ROTs without com-promising data currency of transactions and correctness. Speculation-based approach improves the performance of ROTs by trading extra computing resources without violating serializability as correctness criteria. The simula-tion results show that with the proposed protocol the throughput performance is improved significantly over 2PL and the data currency of ROTs is improved significantly over SI-based ap proaches with manageable extra re-sources.

Abstract

In this paper, we explain a personalized agricultural advisory system called eSagu, which has been developed to improve the performance and utilization of agriculture technology and help Indian farmers. In eSagu, rather than visiting the crop in person, the agricultural expert delivers the expert advice at regular intervals (once in one or two weeks) to each farm by getting the crop status in the form of digital photographs and other information. During 2004-06, through eSagu, agricultural expert advices delivered for about 6000 farms covering six crops. The results show that the expert advices helped the farmers to achieve savings in capital investment and improved the crop yield. Mainly, the data warehouse of farm histories has been developed which is providing the crop related information to the agricultural expert in an integrated manner for generating a quality agricultural expert advice. In this paper, after explaining eSagu and its advantages, we discuss how data warehouse of farm histories is enabling agricultural expert to deliver a quality expert advice. We also discuss some research issues to improve the performance of eSagu.

Abstract

A read-only transaction (ROT) does not modify any data. The main issues regarding processing ROTs are correctness, data currency and perform-ance. Two-phase Locking (2PL) protocol is widely used for concurrency con-trol with serializabilty as correctness criteria. Even though 2PL processes ROTs correctly with no data currency related issues, the performance deteriorates as data contention increases. To improve the performance over 2PL, snapshot iso-lation (SI)-based protocols have been proposed. SI-based protocols process ROTs by reading from a snapshot of the committed data and ignoring the modi-fications produced by the concurrent active transactions. Even though SI-based algorithms improve the performance of ROTs, both data currency of ROTs and correctness (serializability) are compromised. In this paper, we propose an ap-proach to improve the performance of ROTs using speculation without compromising data currency of transactions and correctness. The proposed approach improves the performance of ROTs by trading extra computing re-sources without violating serializability as correctness criteria. The simulation results show that with the proposed protocol the throughput performance is im-proved significantly over 2PL and SI-based approaches with manageable extra resources.