Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Binarization and localization of text images captured on a mobile phone camera

This paper proposes and compares four methods of binarzing text images captured using a camera mounted on a cell phone. The advantages and disadvantages(image clarity and computational complexity) of each method over the others are demonstrated through binarized results. The images are of VGA or lower resolution.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Memory-based reasoning approach for pattern recognition of binary images

Template matching is concerned with measuring the similarity between patterns of two objects. This paper proposes a memory-based reasoning approach for pattern recognition of binary images with a large template set. It seems that memory-based reasoning intrinsically requires a large database. Moreover, some binary image recognition problems inherently need large template sets, such as the recognition of Chinese characters which needs thousands of templates. The proposed algorithm is based on the Connection Machine, which is the most massively parallel machine to date, using a multiresolution method to search for the matching template. The approach uses the pyramid data structure for the multiresolution representation of templates and the input image pattern. For a given binary image it scans the template pyramid searching the match. A binary image of N × N pixels can be matched in O(log N) time complexity by our algorithm and is independent of the number of templates. Implementation of the proposed scheme is described in detail.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

pi.-Facial selectivities in cycloadditions to norbornyl- and norbornenyl-fused p-benzoquinones

The stereochemistry of the Diels-Alder cycloaddition of several dienes to the facially perturbed dienophiles 2,3-norbornenobenzoquinone (3) and 2,3-norbornanobenzoquinone (4) has been examined. Unambiguous structural proof for the adducts formed has been obtained from complementary 'H and I3C NMR spectral data and in two cases through X-ray crystal structure determination. While 1,3-~yclopentadiene1, ,3-~yclohexadienea, nd cyclooctatetraene exhibit preference for addition to 3 from the bottom side, the stereochemical outcome is reversed in their response to 4.1,3-DiphenyIisobenzofuran and 1,2,3,4-tetrachloro-5,5-dimethoxycyclopentadieenneg aged 3 from the top side with marked selectivity, which is further enhanced in their reaction with 4. The observed stereoselectivities seem to be essentially controlled by steric interactons at the transition state. Model calculations provide support for this interpretation.

A simple computational model for predicting .pi.-facial selectivity in reductions of sterically unbiased ketones. Relative importance of electrostatic and orbital interactions

Various factore controlling the preferred facial selectivity in the reductions of a number of sterically unbiased ketones have been evaluated using a semiempirical MO procedure. MNDO optimized geometries do not reveal any significant ground-state distortions which can be correlated with the observed face selectivities. Electrostatic effecta due to an approaching reagent were modeled by placing a test negative charge at a fixed distance from the carbonyl carbon on each of the two faces. A second series of calculations was carried out using the hydride ion as a test nucleophile. The latter calculations effectively include orbital interactions involving the u and u* orbitals of the newly formed bond in the reaction. The computed energy differences with the charge model are generally much larger compared to those with the hydride ion. However, both models lead to predictions which are qualitatively consistent with the experimentally determined facial preferences for most of the systems. Thus, electrostatic interactions between the nucleophile and the substrate seem to effectively determine the face selectivities in these molecules. However, there are a few exceptions in which orbital interactions are found to contribute significantly and occasionally reverse the preference dictated by electrostatic effecta. The remarkable succew of the hydride model calculations, in spite of retaining the unperturbed geometries of the substrates, points to the unimportance of torsional effeds and orbital distortions associated with the pyramidalized carbonyl unit in the transition state in most of the substrates considered. Additional experimental results are reported which provide useful calibration for the present computational approach.

Ground state geometric distortions Image distal substituent effects in determining the β-facial selectivity in 7-norbornenones

The X-ray structure of Image and MNDO optimized geometries of related 7-norbornenone derivatives show a clear tilt of the carbonyl bridge away from the C=C double bond. The preferred reduction from the more hindered face of the diester reveals the electron/electrostatic origin of π - facial selectivity in these systems. X-ray structure and MNDO calculations reveal the dominance of electronic effects in determining the π-facial selectivity in 4a.

Facial Coordination in BIS[BIS(Benzimidazol-2-Ylmethyl)Amine]Copper(II) Perchlorate Dihydrate - Synthesis, Structure, Spectra and Redox Behavior

The 1:1 and 1:2 cooper(II) complexes with the tridentate compound bis(benzimidazol-2-ylmethyl)amine (L(1)) and its benzimidazole (L(2)) and amine (L(3)) N-methyl-substituted derivatives have been prepared and their spectroscopic properties studied. While the 1:1 complexes are of the type CuLX(2) nH(2)O (X = C/O-4(-), NO3-, Cl- or Br-), the 1:2 complexes are of the type CuL(2) (ClO4)(2) nH(2)O (L = L(1) or L(3), n = 0-4). In all these complexes L acts as a tridentate ligand with the amine nitrogen and both the benzimidazole nitrogens co-ordinating to Cu-II. The complex [CuL(2)(1)][ClO4](2) 2H(2)O crystallises in the monoclinic space group P2(1)/c with a = 9.828(2), b = 9.546(2) and c = 19.906(2) Angstrom and beta = 95.71(1)degrees, for Z = 2. The R value is 0.0635 for 2180 significant reflections. The copper(II) ion has an elongated octahedral geometry with four equatorial benzimidazole and two long-distance axial amine N donors. The Cu-N-bzim and Cu-N-amine distances are 2.011(4) and 2.597(6) Angstrom respectively. Factors favouring facial co-ordination to tridentate ligands are discussed. The 1:1 complexes involve meridonal co-ordination of the ligands, with square-based geometry as revealed by ligand-field and EPR spectral properties. The NMe substitution as in CuL(3)(ClO4)(2) confers low V ($) over tilde$$(max) and high E(1/2) for the cu(II)-Cu-I couple. Most of the 1:1 complexes are less reversible but exhibit E(1/2) values more positive than those of the corresponding 1:2 complexes.

Strip based coding for large images using wavelets

Thanks to advances in sensor technology, today we have many applications (space-borne imaging, medical imaging, etc.) where images of large sizes are generated. Straightforward application of wavelet techniques for above images involves certain difficulties. Embedded coders such as EZW and SPIHT require that the wavelet transform of the full image be buffered for coding. Since the transform coefficients also require storing in high precision, buffering requirements for large images become prohibitively high. In this paper, we first devise a technique for embedded coding of large images using zero trees with reduced memory requirements. A 'strip buffer' capable of holding few lines of wavelet coefficients from all the subbands belonging to the same spatial location is employed. A pipeline architecure for a line implementation of above technique is then proposed. Further, an efficient algorithm to extract an encoded bitstream corresponding to a region of interest in the image has also been developed. Finally, the paper describes a strip based non-embedded coding which uses a single pass algorithm. This is to handle high-input data rates. (C) 2002 Elsevier Science B.V. All rights reserved.

Reconstructing Solid Model from 2D Scanned Images of Biological Organs for Finite Element Simulation

This work presents a methodology to reconstruct 3D biological organs from image sequences or other scan data using readily available free softwares with the final goal of using the organs (3D solids) for finite element analysis. The methodology deals with issues such as segmentation, conversion to polygonal surface meshes, and finally conversion of these meshes to 3D solids. The user is able to control the detail or the level of complexity of the solid constructed. The methodology is illustrated using 3D reconstruction of a porcine liver as an example. Finally, the reconstructed liver is imported into the commercial software ANSYS, and together with a cyst inside the liver, a nonlinear analysis performed. The results confirm that the methodology can be used for obtaining 3D geometry of biological organs. The results also demonstrate that the geometry obtained by following this methodology can be used for the nonlinear finite element analysis of organs. The methodology (or the procedure) would be of use in surgery planning and surgery simulation since both of these extensively use finite elements for numerical simulations and it is better if these simulations are carried out on patient specific organ geometries. Instead of following the present methodology, it would cost a lot to buy a commercial software which can reconstruct 3D biological organs from scanned image sequences.