In this paper, we present an evaluative study of pixel-labeling methods using the HBA 1.0 dataset for historical book analysis. This study is held in the context of the 2nd historical book analysis (HBA2019) competition and in conjunction with the 15th IAPR international conference on document analysis and recognition (ICDAR2019). The HBA2019 competition provides a large experimental corpus and a thorough evaluation protocol to ensure an objective performance benchmarking of pixel-labeling document image methods. Two nested challenges are evaluated in the HBA2019 competition: Challenge 1 and Challenge 2. Challenge 1 evaluates how image analysis methods could discriminate the textual content from the graphical ones at pixel level. Challenge 2 assesses the capabilities of pixel-labeling methods to separate the textual content according to different text fonts (e.g. lowercase, uppercase, italic, etc.) at pixel level. During the competition, we received 52 and 38 different teams' registrations for Challenge 1 and Challenge 2, respectively and finally 5 of them submitted their results in each challenge. Qualitative and numerical results of the participating methods in both challenges are reported and discussed in this paper in order to provide a baseline for future evaluation studies in historical document image analysis. The evaluation shows that the method submitted by the NLPR-CASIA team achieves the highest performance in both challenges.

Through the mid-nineties, handwriting recognition was performed with segmentation-based approaches, which rely on hand-crafted and script-specific segmentation algorithms to isolate characters for subsequent recognition. These systems have since been eclipsed by segmentation-free systems that learn to segment implicitly and to recognize characters in a sequential signal automatically. It may be easy therefore to consider character segmentation as a solved problem. However, we show that for small training datasets, there is potential to leverage either manually- or automatically- provided segmentation information to guide the learning process in segmentation-free systems, leading to significantly higher generalization accuracy using few training exemplars. We show that providing just a small amount of segmentation information by isolating and synthesizing new sequences from a handwriting sample allows very small annotated datasets to achieve accuracy comparable to that of much larger annotated datasets at only a fraction of the human effort. We provide validation for this technique on the George Washington Papers word database, a similarly sized journal word corpus (the Smith collection), subsets of the well-known IAM handwriting database, and sequences of MNIST handwritten digits.

We introduce two data augmentation and normalization techniques, which, used with a CNN-LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting recognition tasks. (1) We apply a novel profile normalization technique to both word and line images. (2) We augment existing text images using random perturbations on a regular grid. We apply our normalization and augmentation to both training and test images. Our approach achieves low WER and CER over hundreds of authors, multiple languages and a variety of collections written centuries apart. Image augmentation in this manner achieves state-of-the-art recognition accuracy on several popular handwritten word benchmarks.

We present a novel method of offline whole-word handwriting recognition. We use automatic image morphing to compute 2-D geometric warps that align the strokes of each word image with the strokes of word images of training examples. Once the strokes of a given word are aligned to a training example, we use distance maps to compare how similar the two words are. Like 1-D Dynamic Programming (DP) methods, our warp-based method is robust to limited variation in word length and letter spacing. However, due to its 2-D nature, our method is also more robust than 1-D DP methods in handling variations caused by additional inconsistencies in character shape and stroke placement. Although we use DP for coarse alignment, the novel contribution of this paper is not 2-D DP, but morphing to automatically discover an actual 2-D mesh-based warp, followed by the use of distance maps to compute similarity between words. Early results are encouraging. On two datasets (1,000 training and 1,000 test words each), we get 88.77% and 89.33% recognition accuracy for in-vocabulary words. These are increases of 7.89% and 17.16% above the results of a 1-D DP approach.

We employ Eigenfaces to discriminate between handwritten and machine-printed text at the connected component (CC) level. Normalized images of machine print CCs are treated as points in a high-dimensional space. PCA yields a reduced-dimensional character space. Representative machine print CCs are projected into character space and a local distance threshold for each representative is automatically determined. CCs are classified as machine print if they are within the local distance threshold of their closest machine print representative. Otherwise, they are classified as handwriting. Recursive character segmentation using min graph cut is used to address the problem of touching characters. Validation over a large NIST handwriting and machine print database demonstrates precision of 93.98% and 89.1% for machine print and handwriting respectively.

The use of digital images of scanned handwritten historical documents has increased in recent years, especially with the online availability of large document collections. However, the sheer number of images in some of these collections makes them cumbersome to manually read and process, making the need for automated processing of increased importance. A key step in the recognition and retrieval of such documents is binarization, the separation of document text from the page's background. Binarization of images of historical documents that have been affected by degradation or are otherwise of poor image quality is difficult and continues to be a topic of research in the field of image processing. This paper presents a novel approach to this problem, including two primary variations. One combines a recursive extension of Otsu thresholding and selective bilateral filtering to allow automatic binarization and segmentation of handwritten text images. The other also builds on the recursive Otsu method and adds improved background normalization and a post-processing step to the algorithm to make it more robust and to perform adequately even for images that present bleed-through artifacts. Our results show that these techniques segment the text in historical documents comparable to and in some cases better than many state-of-the-art approaches based on their performance as evaluated using the dataset from the recent ICDAR 2009 Document Image Binarization Contest.

This paper describes "Just-In-Time Browsing" (JITB), a method for image browsing (at modem-like speed) in which image data is transmitted and presented to the user progressively, in prioritized order, based on image content and user interaction. Spatial resolution and grayscale or color fidelity is increased first for the portions of the image that are immediately of most interest to the user. JITB is specifically geared toward digitized microfilm and other similar document image collections, although it can also be used for other types of images. A series of common browsing tasks performed by multiple users demonstrates that JITB compares favorably with two other methods that are currently used for browsing images on the Internet.

Zoning documents increases the resolution of indexing from the image level to the field level. A line-delimited tabular document forms a well defined series of regions. However, as image quality decreases, accurate zoning becomes increasingly difficult. Given a sequence of documents with the same layout, we present a robust zoning method which exploits both intra- and inter-document consensus to form a more accurate combined result (template) that can be applied to any other document with the same layout.

We introduce Object-Based Image Editing (OBIE) for real-time animation and manipulation of static digital photographs. Individual image objects (such as an arm or nose, Figure 1) are selected, scaled, stretched, bent, warped or even deleted (with automatic hole filling ) - at the object, rather than the pixel level - using simple gesture motions with a mouse. OBIE gives the user direct, local control over object shape, size, and placement while dramatically reducing the time required to perform image editing tasks.Object selection is performed by manually collecting (subobject) regions detected by a watershed algorithm. Objects are tessellated into a triangular mesh, allowing shape modification to be performed in real time using OpenGL's texture mapping hardware.Through the use of anchor points, the user is able to interactively perform editing operations on a whole object, or just part(s) of an object - including moving, scaling, rotating, stretching, bending, and deleting. Indirect manipulation of object shape is also provided through the use of sliders and Bezier curves. Holes created by movement are filled in real-time based on surrounding texture.When objects stretch or scale, we provide a method for preserving texture granularity or scale. We also present a texture brush, which allows the user to "paint" texture into different parts of an image, using existing image texture(s).OBIE allows the user to perform interactive, high-level editing of image objects in a few seconds to a few ten's of seconds

We exploit the Accumulator Array of the Hough Transform by finding collections of (2 or more) peaks through which a given sinusoid will pass. Such sinusoids identify points in the original image where lines intersect. Peak collection (or line aggregation) is performed by making a second pass through the edge map, but instead of laying points down in the accumulator array (as with the original Hough Transform), we compute the line integral over each sinusoid that corresponds to the current edge point. If a sinusoid passes through /spl ges/2 peaks, we deposit that sum/integral into a new accumulator array - an array that has a direct one-to-one correspondence with the original image. Thus, "Houghing the Hough" identifies points that correspond to corners, junctions or line intersections in image space. During initial peak collection, we include in the line integral only the most (locally) significant peaks while sifting out other (comparatively) weaker peaks from the current as well as subsequent peak collections. This "contextual peak sifting" greatly reduces computation, the effect of noise and the occurrence of false positives. Virtual line intersections (vanishing points, occluded corners, etc.) are detected as peaks without proximate edge support. Results in real-world images show the technique performs well in identifying corners, junctions and intersecting lines in a variety of scenes containing manmade objects such as buildings, documents, etc.

Accurate estimation of effective camera focal length is crucial to the success of panoramic image stitching. Fast techniques for estimating the focal length exist, but are dependent upon a close initial approximation or the existence of a full circle panoramic image sequence. Numerical solutions of the focal length demonstrate strong coupling between the focal length and the angles used to position each component image about the common spherical center. This paper demonstrates that parameterizing panoramic image positions using spherical arc length instead of angles effectively decouples the focal length from the image position. This new parameterization does not require an initial focal length estimate for quick convergence, nor does it require a full circle panorama in order to refine the focal length. Experiments with synthetic and real image sets demonstrate the robustness of the method and a speedup of 5 to 20 times over angle based positioning.

We introduce a confidence measure that estimates the assurance that a graph arc (or edge) corresponds to an object boundary in an image. A weighted, planar graph is imposed onto the watershed lines of a gradient magnitude image and the confidence measure is a function of the cost of fixed-length paths emanating from and extending to each end of a graph arc. The confidence measure is applied to automate the detection of object boundaries and thereby reduces (often greatly) the time and effort required for object boundary definition within a user guided image segmentation environment.

Intelligent Scissors and Intelligent Paint are complementary interactive image segmentation tools that allow a user to quickly and accurately select objects of interest. This demonstration provides a means for participants to experience the dynamic nature of these tools.

Intelligent Scissors is an interactive image segmentation tool that allows a user to select piece-wise globally optimal contour segments that correspond to a desired object boundary. We present a new and faster method of computing the optimal path by over-segmenting the image using tobogganing and then imposing a weighted planar graph on top of the resulting region boundaries. The resulting region-based graph is many times smaller than the previous pixel-based graph, thus providing faster graph searches and immediate user interaction. Further tobogganing provides an new systematic and predictable framework for computing edge model parameters, allowing subpixel localization as well as a measure of edge blur.

Summary form only given. We present a new method for representation and (lossy) compression of bitonal document images. The technique extracts a skeletal medial axis from each object using a true Euclidean distance map of the image and then finds piecewise linear breakpoints in the skeleton to create a breakpoint skeletal representation, b.p.s. The b.p.s. is encoded for each object as a set of triples. The original binary object is reconstructed by first reconstructing the skeleton using linear interpolation between breakpoints and then fractionally dilating each point on the skeleton with the (linearly interpolated) radius, r/sub i/. For noninteger r/sub i/ fractional dilation provides a natural antialiasing in the reconstructed image. Breakpoints can be extracted to preserve fine detail or a more coarse representation by tightening or relaxing the pruning radius respectively. If, in extracting breakpoints, the pruning radius is set to zero, the reconstruction is almost lossless, but the compression is worse.

We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition.Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable.However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse.When the gestured mouse position comes in proximity to an object edge, a live-wire boundary "snaps" to, and wraps around the object of interest.Live-wire boundary detection formulates discrete dynamic programming (DP) as a two-dimensional graph searching problem.DP provides mathematically optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures.Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood.Boundary cooling automatically freezes unchanging segments and automates input of additional seed points.Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted.Extracted objects can be scaled, rotated, and composited using live-wire masks and spatial frequency equivalencing.Frequency equivalencing is performed by applying a Butterworth filter which matches the lowest frequency spectra to all other image components.Intelligent Scissors allow creation of convincing compositions from existing images while dramatically increasing the speed and precision with which objects can be extracted.

We present a new method for creating n-dimensional rotation matrices from manipulating the projections of n-dimensional data coordinate axes onto a viewing plane. A user interface for n-dimensional rotation is implemented. The interface is shown to have no rotational hysteresis.>

article Free Access Share on Interactive measurement of three-dimensional objects using a depth buffer and linear probe Authors: Shawn C. Becker Brigham Young Univ., Provo, UT Brigham Young Univ., Provo, UTView Profile , William A. Barrett Brigham Young Univ., Provo, UT Brigham Young Univ., Provo, UTView Profile , Don R. Olsen Brigham Young Univ., Provo, UT Brigham Young Univ., Provo, UTView Profile Authors Info & Claims ACM Transactions on GraphicsVolume 10Issue 2pp 200–207https://doi.org/10.1145/108360.108446Published:01 April 1991Publication History 2citation475DownloadsMetricsTotal Citations2Total Downloads475Last 12 Months46Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The card-changeable permanent-magnet-twistor memory is a large capacity (ca 105bits) storage media for information that is infrequently changed. The information is stored in the form of small bar magnets bonded to a removable plastic card. The magnets, when magnetized, inhibit the switching of a section of twistor wire at a twistor-wire-solenoid crosspoint. For maximum information density the magnet shape and strength must be optimized with respect to the magnet's action on the inhibited crosspoint and the fringing action on neighboring crosspoints. The objective is a magnet with a small dipole moment, but with adequate inhibition of the twistor switching over a reasonable range of misposition. Suitable magnet shapes and a general discussion of the stray fields in a large array of magnets are given. For maximum capacity, the transmission characteristics of the twistor wire and the character of the access switch must be considered. Two novel structures of this memory permit increased information density and capacity. The feasibility of a random-access high-density memory submodule containing 360,000 bits in 0.7 foot3 with a cycle time of 5 μsec has been demonstrated.