Decision-Based Specification and Comparison of Table Recognition Algorithms

The vast majority of algorithms in the table recognition literature are specified informally as a sequence of operations [7]. This has the undesirable side effects that models of table structure are implicit, defined generatively by the sequence of operations, and that the effects of intermediate decisions are often lost as usually a single interpretation is modified in-place. We wished to compare the Handley [2] and Hu et al. [4]. table structure recognition algorithms and the complete set of table cell hypotheses they each generated, including any rejected in the final result. Rebuilding the systems using procedural code that transformed data structures for interpretations in-place would not have achieved this goal. Initially we translated the strategies to a formal model-based (specifically grammarbased) framework. A well designed model-driven system (such as DMOS by Couasnon ¨ [1]) makes it easier to observe and record decision making, and can be programmed succinctly by a model specification. However, we found mapping the sequence of operations in the strategies to a model based description was difficult, and the formal system required frequent and substantial reconfiguration in order to incorporate unanticipated requirements. We then considered an intermediate level of formalization. By using a small set of basic graph-based operations we could define recognition algorithms as a series of decisions, where the alternatives for each decision were model operations of a specified type (e.g. classifying table cells as header cells or data cells). This made the model operations considered and applied at each decision point explicit, permitted dependencies between logical types to be automatically recovered, and allowed the complete history of hypothesis creation, rejection, and reinstatement to be automatically captured. The resulting formalization is the Recognition Strategy Language (RSL)

Representation and Three-Dimensional Interpretation of Image Texture: An Integrated Approach

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryAir Force Office of Scientific Research / AFOSR 87-0100Eastman Koda

Recovering the Orientation of Textured Surfaces in Natural Scenes

Coordinated Science Laboratory changed its name from Control Systems LaboratoryAir Force Office of Scientific Research / AFOSR 82-0317IBM fellowshipU of I OnlyRestricted to UIUC communit

General Diagram-Recognition Methodologies

this paper. While we find it useful to discuss diagram recognition in terms of the above six processes, the processes are not necessarily clearly delineated in an implementation, and they need not be performed in the indicated order. For example, partial identification of spatial and logical relationships can be performed prior to symbol recognition, as in math layout analysis [OkMi91]. After symbol identity is known, more detailed identification of these relationships is possible. (The identity of a symbol helps to determine which spatial and logical relationships are most meaningful. For example, in music notation the vertical location of a notehead or accidental conveys critical information, whereas the vertical location of a stem-end does not.) Concurrency among the six recognition processes is possible, and can be useful in handling ambiguity, noise, and uncertainty. Two basic control strategies for handling uncertainty are: . Sequential processing with lists of alternatives. All possible interpretations are carried along from one recognition stage to the next. For example, the symbol recognizer can produce a list of alternatives (including "noise") for each symbol it recognizes. During symbol-arrangement analysis, constraints are applied to eliminate alternative interpretations. Interleaving of constraint-application and information recovery may be required, since some constraints can only be applied once partial recognition results are obtained [Fahm95]. . Contextual feedback. Concurrent execution of recognition processes permits higher-level, contextual feedback to compensate for noisy input or to reject erroneous input. Blackboard systems, for example, provide such a control mechanism. Sequential processing is used in [BaIt94], with some discussion of limitatio..

Recovering the Orientation of Textured Surfaces in Natural Scenes (Image Processing)

148 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.A perspective view of a slanted textured surface shows systematic changes in the density, area and aspect-ratio of texture elements. These apparent changes in texture element properties can be analyzed to recover information about the physical layout of the scene. However, in practice it is difficult to identify texture elements, especially in images where the texture elements are partially occluded or are themselves textured at a finer scale. To solve this problem, it is necessary to integrate the extraction of texture elements with the recognition of scene layout. This thesis presents a method for recovering the orientation of textured surfaces while simultaneously identifying texture elements. Candidate texture elements are constructed from overlapping circular regions of relatively uniform gray-level. The uniform circular regions are found by convolving the image with \nabla\sp2G (Laplacian-of-Gaussian) masks over a range of scales, and comparing the convolution output to that expected for a circular disk of constant gray level. True texture elements are selected from the set of candidate texture elements by finding the planar surface that best predicts the properties of the candidate texture elements. Each planar fit is evaluated by comparing the predicted texture-element areas to the actual areas of the candidate texture elements. The planar fit receiving support from the most regions in chosen as the correct interpretation. Simultaneously, those candidate texture elements that support the best plane are identified as the true texture elements. Results are shown on images of many natural textures, including rocks, leaves, waves, flowers, bark, and clouds. Textures consist of both bright and dark regions, corresponding to lit and shadowed areas, or to foreground and background. The positive-contrast and negative-contrast regions of each image are analyzed separately. The two analyses often result in slant and tilt estimates that are within ten degrees of each other; images where the discrepancy is larger have specific textural properties that cause inaccuracies in one or both of the analyses.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

IJDAR DOI 10.1007/s10032-006-0020-2 ORIGINAL ARTICLE

A survey of document image classification: problem statement, classifier architecture and performance evaluatio

Issues in Performance Evaluation: A Case Study of Math Recognition

Performance evaluation of document recognition systems is a difficult and practically important problem. Issues arise in defining requirements, in characterizing the system’s range of inputs and outputs, in interpreting published performance evaluation results, in reproducing performance evaluation experiments, in choosing training and test data, and in selecting performance metrics. We discuss these issues in the context of evaluating systems for recognition of mathematical expressions. Excellent progress has been made in the theory and practice of performance evaluation, but many open problems remain