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We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a ''phrase-based'' system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.

Minutiae based fingerprint matching algorithms are wildly used in fingerprint identification and verification applications. However, they may suffer from spurious matches because they do not use the rich local image information. In this paper, we extend minutiae based methods to incorporate such local image information. Our method uses local mutual information, a proven similarity measure in various applications, to improve the matching rate. The overall minutiae distribution pattern between two fingerprints is represented by the initial minutiae matching result, while the mutual information measures the similarity between neighborhoods of matched minutiae, thus enhancing the final matching decision. FVC2002 DB1 and DB3 databases are used to test the proposed approach. Experimental result shows the improvement when combining minutiae matching scores with mutual information scores.

Despite several decades of research in handwriting recognition, the goal of having computers access handwritten information from unconstrained document images is still elusive. Current handwriting recognition systems are only capable of recognizing words that are present in a restricted lexicon typically comprised of 10 to 1000 words. As the size of the lexicon grows, the recognition accuracy falls sharply and is reported to be around 30% for a10K word lexicon. The objective of this research is to raise the accuracy levels on unconstrained handwritten documents by reducing the size of lexicons. We present an innovative method of lexicon reduction by topic categorization of handwritten documents. After categorization of a document into a topic e.g. sports, science etc. we use smaller lexicons that include only words with high mutual information with that topic and hence increase performance of recognizers. In this paper we present different techniques and report results on a publicly available dataset.

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers produce reasonably clean output when used with a restricted lexicon. But in absence of such a restricted lexicon, the output of an unconstrained handwritten word recognizer is noisy. The objective of this research is to process noisy recognizer output and eliminate spurious recognition choices using a topic based language model. We construct a topic based language model for every document using a training data which is manually categorized. A topic categorization sub-system based on Maximum Entropy model is also trained which is used to generate the topic distribution of a test document. A given test word image is processed by the recognizer and its word recognition likelihood is refined by incorporating topic distribution of the document and topic based language model probability. The proposed method is evaluated on a publicly available IAM dataset and experimental results show significant improvement in the word recognition accuracy from 32% to 40% over a test set consisting of 4033 word images extracted from 70 handwritten document images.

The security, lack of anonymity and revocability of the biometric template are critical issues that need to be addressed in order to vindicate the viability of biometric based authentication systems. Several methods have been proposed to address these problems. However, most of these methods offer lower accuracies than the base system where the template is insecure. This is because in most systems the gain in the security is achieved as a result of loss in non-redundant information. In this paper, we describe tradeoff between accuracy of an anonymous system and the security of the biometrics system. As a case study we start with a highly secure representation of a fingerprint. Then we describe several methods and show experimental results proving that every time we add more information to the secure representation, the accuracy increases, however valuable information is revealed to an adversary. We propose, via a k-trial attack model, how the trade-off can be complemented by another security policy such as an account lockout after a given number of attempts.

Biometric identification has numerous advantages over conventional ID and password systems; however, the lack of anonymity and revocability of biometric templates is of concern. Several methods have been proposed to address these problems. Many of the approaches require a precise registration before matching in the anonymous domain. We introduce binary string representations of fingerprints that obviates the need for registration and can be directly matched. We describe several techniques for creating anonymous and revocable representations using these binary string representations. The match performance of these representations is evaluated using a large database of fingerprint images. We prove that given an anonymous representation, it is computationally infeasible to invert it to the original fingerprint, thereby preserving privacy. To the best of our knowledge, this is the first linear, anonymous and revocable fingerprint representation that is implicitly registered.

Automatic language identification is an important step prior to optical character recognition (OCR). In this paper we present a system to discriminate between Arabic and Persian in historical Afghan manuscripts. The classification is performed at a sub-sentence level. We propose a feature extraction algorithm for a sub-sentence based on Gabor filters followed by classification using a support vector machine (SVM). An overall precision of 96.72% and 94.90% is obtained for Persian and Arabic respectively.

An intrusion is defined as any set of actions that compromise the integrity, confidentiality or availability of a resource. Intrusion detection is an important task for information infrastructure security. One major challenge in intrusion detection is that we have to identify the camouflaged intrusions from a huge amount of normal communication activities. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. It is demanding to apply data mining techniques to detect various intrusions. In the last several years, some exciting and important advances have been made in intrusion detection using data mining techniques. Research results have been published and some prototype systems have been established. Inspired by the huge demands from applications, the interactions and collaborations between the communities of security and data mining have been boosted substantially. This seminar will present an interdisciplinary survey of data mining techniques for intrusion detection so that the researchers from computer security and data mining communities can share the experiences and learn from each other. Some data mining based intrusion detection systems will also be reviewed briefly. Moreover, research challenges and problems will be discussed so that future collaborations may be stimulated. For data mining/database researchers and practitioners, the seminar will provide background knowledge and opportunities for applying data mining techniques to intrusion detection and computer security. For computer security researchers and practitioners, it provides knowledge on how data mining can benefit and enhance computer security. We will try to understand and appreciate the following technical issues.

In order to improve the readability and the automatic recognition of handwritten document images, preprocessing steps are imperative. These steps in addition to conventional steps of noise removal and filtering include text normalization such as baseline correction, slant normalization and skew correction. These steps make the feature extraction process more reliable and effective. Recently Arabic handwriting recognition has received some attention from the research community. Due to the unique nature of the script, the conventional methods do not prove to be effective. In our work, we describe an orientation independent technique for baseline detection of Arabic words. In addition to that we describe, in the rest of the paper, our techniques for slant normalization, slope correction, line and word separation in handwritten Arabic documents. We show how the baseline can be exploited for slope and skew correction before proceeding with the steps of line and word separation.

We present methodologies for three important tasks that will eventually enable digital access of multilingual Indian document images. First, we describe several document image analysis techniques necessary to prepare Devanagari document images for OCR. The second task is OCR for machine printed Devanagari words without the help of a lexicon. We describe the OCR methodology and show how it is being extended to other Indian languages. Finally, we describe a versatile platform that facilitates automatic segmentation of document images in multiple Indian languages and an interface to capture the ground truth corresponding to the text. We use transliterated English text and virtual keyboards in a range of Indian languages for this purpose. The multilingual data entry capabilities of the tool and its underlying UNICODE data representation within a structured XML document also allow users to annotate passages of text in one language in other languages using a markup scheme to switch between scripts. Text and annotations are rendered in the appropriate scripts as the text is being annotated, thus providing users prompt and natural feedback. The XML back-end allows meta-data to be recorded describing the annotated document.

In Codice Ratio is a research project to study tools and techniques for analyzing the contents of historical documents conserved in the Vatican Secret Archives (VSA). In this paper, we present our efforts to develop a system to support the transcription of medieval manuscripts. The goal is to provide paleographers with a tool to reduce their efforts in transcribing large volumes, as those stored in the VSA, producing good transcriptions for significant portions of the manuscripts. We propose an original approach based on character segmentation. Our solution is able to deal with the dirty segmentation that inevitably occurs in handwritten documents. We use a convolutional neural network to recognize characters and language models to compose word transcriptions. Our approach requires minimal training efforts, making the transcription process more scalable as the production of training sets requires a few pages and can be easily crowdsourced. We have conducted experiments on manuscripts from the Vatican Registers, an unreleased corpus containing the correspondence of the popes. With training data produced by 120 high school students, our system has been able to produce good transcriptions that can be used by paleographers as a solid basis to speedup the transcription process at a large scale., Donatella Firmani, Marco Maiorino, Paolo Merialdo, and Elena Nieddu. 2018. Towards Knowledge Discovery from the Vatican Secret Archives. In Codice Ratio - Episode 1: Machine Transcription of the Manuscripts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 263-272