Jianying Hu

On-line handwritten signature verification using hidden Markov model features

A method for the automatic verification of on-line handwritten signatures using both global and local features as described. The global and local features capture various aspects of signature shape and dynamics of signature production. The authors demonstrate that with the addition to the global features of a local feature based on the signature likelihood obtained from hidden Markov models (HMM) the performance of signature verification improves significantly. The current version of the program, has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.

Evaluating the performance of table processing algorithms

Abstract. While techniques for evaluating the performance of lower-level document analysis tasks such as optical character recognition have gained acceptance in the literature, attempts to formalize the problem for higher-level algorithms, while receiving a fair amount of attention in terms of theory, have generally been less successful in practice, perhaps owing to their complexity. In this paper, we introduce intuitive, easy-to-implement evaluation schemes for the related problems of table detection and table structure recognition. We also present the results of several small experiments, demonstrating how well the methodologies work and the useful sorts of feedback they provide. We first consider the table detection problem. Here algorithms can yield various classes of errors, including non-table regions improperly labeled as tables (insertion errors), tables missed completely (deletion errors), larger tables broken into a number of smaller ones (splitting errors), and groups of smaller tables combined to form larger ones (merging errors). This leads naturally to the use of an edit distance approach for assessing the results of table detection. Next we address the problem of evaluating table structure recognition. Our model is based on a directed acyclic attribute graph, or table DAG. We describe a new paradigm, “graph probing,” for comparing the results returned by the recognition system and the representation created during ground-truthing. Probing is in fact a general concept that could be applied to other document recognition tasks as well.

Why table ground-truthing is hard

The principle that for every document analysis task there exists a mechanism for creating well-defined ground-truth is a widely held tenet. Past experience with standard datasets providing ground-truth for character recognition and page segmentation tasks supports this belief. In the process of attempting to evaluate several table recognition algorithms we have been developing, however, we have uncovered a number of serious hurdles connected with the ground-truthing of tables. This problem may, in fact, be much more difficult than it appears. We present a detailed analysis of why table ground-truthing is so hard, including the notions that there may exist more than one acceptable truth and/or incomplete or partial truths.

Matching and retrieval based on the vocabulary and grammar of color patterns

While it is recognized that images are described through color, texture and shapes of objects in the scene, general image understanding is still difficult. Thus, to perform image retrieval in a human-like manner one has to choose a specific domain, understand how users achieve similarity within that domain and then build a system that duplicates human performance. Since color and texture are fundamental aspects of human perception we propose a set of techniques for retrieval of color patterns. To determine how humans judge similarity of color patterns we performed a subjective study. Based on the results of the study five most relevant visual categories for the perception of pattern similarity were identified. We also determined the hierarchy of rules governing the use of these categories. Based on these results we designed a system which accepts one or more texture images as input, and depending on the query, produces a set of choices that follow human behavior in pattern matching. Processing steps in our model follow those of the human visual system, resulting in perceptually based features and distance measures. As expected, search results closely correlate with human choices.

Document Analysis Systems V

Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image.

A Hidden Markov Model approach to online handwritten signature verification

Abstract. A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to the global features of a signature, significantly improves the performance of verification. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.

Medium-independent table detection

An important step towards the goal of table understanding is a method for reliable table detection. This paper describes a general solution for detecting tables based on computing an optimal partitioning of a document into some number of tables. A dynamic programming algorithm is given to solve the resulting optimization problem. This high-level framework is independent of any particular table quality measure and independent of the document medium. Moreover, it does not rely on the presence of ruling lines or other table delimiters. We also present table quality measures based on white space correlation and vertical connected component analysis. These measures can be applied equally well to ASCII text and scanned images. We report on some preliminary experiments using this method to detect tables in both ASCII text and scanned images, yielding promising results. We present detailed evaluation of these results using three different criteria which by themselves pose interesting research questions.

Comparison and Classification of Documents Based on Layout Similarity

This paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. These fixed-length vectors are then compared to each other through a Manhattan distance computation for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval coding in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in the paper can be used in various document retrieval tasks including visual similarity based retrieval, categorization and information extraction.

Table structure recognition and its evaluation

Tables are an important means for communicating information in written media, and understanding such tables is a challenging problem in document layout analysis. In this paper we describe a general solution to the problem of recognizing the structure of a detected table region. First hierarchial clustering is used to identify columns and then spatial and lexical criteria to classify headers. We also address the problem of evaluating table structure recognition. Our model is based on a directed acyclic attribute graph, or table DAG. We describe a new paradigm, random graph probing, for comparing the results returned by the recognition system and the representation created during ground-truthing. Probing is in fact a general concept that could be applied to other document recognition tasks and perhaps even other computer vision problems as well.

Identifying story and preview images in news web pages

The World Wide Web provides an increasingly powerfuland popular publication mechanism. Web documents oftencontain a large number of images serving various differentpurposes. This paper focuses on images that are associatedwith a story or preview to a story. Such images often accompanythe key content on a web page, thus their identificationis important for applications such as web page summarizationand mobile access. We present a novel algorithmfor automatic identification of story/preview images whichcombines features extracted from both the image itself andthe surrounding text. The effectiveness of this algorithm isdemonstrated by experimental results on over 1500 imagescollected from 25 news web sites.