We describe a robust and efficient system for recognizing typeset and handwritten mathematical notation. From a list of symbols with bounding boxes the system analyzes an expression in three successive passes. The Layout Pass constructs a Baseline Structure Tree (BST) describing the two-dimensional arrangement of input symbols. Reading order and operator dominance are used to allow efficient recognition of symbol layout even when symbols deviate greatly from their ideal positions. Next, the Lexical Pass produces a Lexed BST from the initial BST by grouping tokens comprised of multiple input symbols; these include decimal numbers, function names, and symbols comprised of nonoverlapping primitives such as "=". The Lexical Pass also labels vertical structures such as fractions and accents. The Lexed BST is translated into L A T E X. Additional processing, necessary for producing output for symbolic algebra systems, is carried out in the Expression Analysis Pass. The Lexed BST is translated into an Operator Tree, which describes the order and scope of operations in the input expression. The tree manipulations used in each pass are represented compactly using tree transformations. The compiler-like architecture of the system allows robust handling of unexpected input, increases the scalability of the system, and provides the groundwork for handling dialects of mathematical notation.

Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables, and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies the decisions that are made by a table recognizer, and the assumptions and inferencing techniques that underlie these decisions.

this paper, we use the following five-step methodology to quantitatively compare the performance of page segmentation algorithms: 1) First, we create mutually exclusive training and test data sets with groundtruth, 2) we then select a meaningful and computable performance metric, 3) an optimization procedure is then used to search automatically for the optimal parameter values of the segmentation algorithms on the training data set, 4) the segmentation algorithms are then evaluated on the test data set, and, finally, 5) a statistical and error analysis is performed to give the statistical significance of the experimental results. In particular, instead of the ad hoc and manual approach typically used in the literature for training algorithms, we pose the automatic training of algorithms as an optimization problem and use the Simplex algorithm to search for the optimal parameter value. A paired-model statistical analysis and an error analysis are then conducted to provide confidence intervals for the experimental results of the algorithms. This methodology is applied to the evaluation of five page segmentation algorithms of which, three are representative research algorithms and the other two are well-known commercial products, on 978 images from the University of Washington III data set. It is found that the performance indices (average textline accuracy) of the Voronoi, Docstrum, and Caere segmentation algorithms are not significantly different from each other, but they are significantly better than that of ScanSoft's segmentation algorithm, which, in turn, is significantly better than that of X-Y cut

In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user’s specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock “wood workshop ” assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, ham-mering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and 3-axis accelerometers mounted at two positions on the user’s arms. Potentially “interesting ” activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov

There is an established need for objective evaluation of layout analysis methods, in realistic circumstances. This paper describes the Page Segmentation Competition (modus operandi, dataset and evaluation criteria) held in the context of ICDAR2005 and presents the results of the evaluation of four candidate methods. The main objective of the competition was to compare the performance of such methods using scanned documents from commonlyoccurring publications. The results indicate that although methods seem to be maturing, there is still a considerable need to develop robust methods that deal with everyday documents. 1

We consider the relationship between the performance characteristics of vision algorithms and algorithm design. In the first part we discuss the issues involved in testing. A description of good practice is given covering test objectives, test data, test metrics and the test protocol. In the second part we discuss aspects of good algorithmic design including understanding of the statistical properties of data and common algorithmic operations, and suggest how some common problems may be overcome. 1

This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of ICDAR2007. The aim of this contest was to use well established evaluation practices and procedures in order to record recent advances in offline handwriting segmentation. Two benchmarking datasets (one for text line and one for word segmentation) were used in a common evaluation platform in order to test and compare all submitted algorithms for handwritten document segmentation in realistic circumstances. The results of the evaluation of five algorithms submitted by participants as well as of two state-of-the-art algorithms are presented. The performance evaluation method is based on counting the number of matches between the text lines or words detected by the algorithms and the text line or words of the ground truth. 1.

This paper continues the authors ’ attempt to address the need for objective comparative evaluation of layout analysis methods in realistic circumstances. It describes the Page Segmentation Competition (modus operandi, dataset and evaluation criteria) held in the context of ICDAR2007 and presents the results of the evaluation of three candidate methods. The main objective of the competition was to compare the performance of such methods using scanned documents from commonlyoccurring publications. The results indicate that although methods continue to mature, there is still a considerable need to develop robust methods that deal with everyday documents. 1

Abstract. In this paper we present a system to build synthetic graphical documents for the performance evaluation of symbol recognition systems. The key contribution of this work is the building of whole document like drawings or maps. We exploit the layer property of graphical documents by putting symbol sets in different ways from a same background using positioning constraints. Experiments are presented to build two kinds of test document databases: bags of symbol and architectural drawings. 1

Document page segmentation is a crucial preprocessing step in Optical Character Recognition (OCR) systems. While numerous page segmentation algorithms have been proposed, there is relatively less literature on comparative evaluation --- empirical or theoretical --- of these algorithms. For the existing performance evaluation methods, two crucial components are usually missing: 1) automatic training of algorithms with free parameters and 2) statistical and error analysis of experimental results. In this thesis, we use the following five-step methodology to quantitatively compare the performance of page segmentation algorithms: 1) First we create mutually exclusive training and test datasets with groundtruth, 2) we then select a meaningful and computable performance metric, 3) an optimization procedure is then used to search automatically for the optimal parameter values of the segmentation algorithms, 4) the segmentation algorithms are then evaluated on the test dataset, and finally 5) ...