nils-herrmann/clean_scirepeval huggingface dataset:
Document Image Recognition Based on Template Matching of Component Block Projections

Document Image Recognition (DIR), a very useful technique in office automation and digital library applications, is to find the most similar template for any input document image in a prestored template document image data set. Existing methods use both local features and global layout information. In this paper, we propose a novel algorithm based on the global matching of Component Block Projections (CBP), which are the concatenated directional projection vectors of the component blocks of a document image. Compared to those existing methods, CBP-based template-matching methods possess two major advantages: (1) The spatial relationship among the component blocks of a document image is better represented, hence a very high matching accuracy can be obtained even for a large template set and seriously distorted input images; and (2) the effective matching distance of each template and the triangle inequality are proposed to significantly reduce the computational cost. Our experimental results confirm these advantages and show that the CBP-based template-matching methods are very suitable for DIR applications.
Structural representation and BPTS learning for shape classification

In this paper, a novel shape classification technique based on a hierarchical shape representation and the back-propagation through structure (BPTS) learning algorithm is proposed. In our representation scheme, a shape is hierarchically represented with the segments composing the contour of the shape by using a scale-space filtering method. The BPTS algorithm is then applied to learn to classify shapes with such a tree-structure representation. Simulations on both artificially generated shape patterns and real world gesture patterns show that robust classification results can be achieved by using a small set of features only.
Extraction and Optimization of B-Spline PBD Templates for Recognition of Connected Handwritten Digit Strings

The recognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. The authors first present a rational B-spline representation of digit templates based on Pixel-to-Boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evolutionary algorithm to optimize these templates. In total, 1000 templates (100 templates for each of 10 classes) were extracted from and optimized on 10426 training samples from the NIST Special Database 3. By using these templates, a nearest neighbor classifier can successfully reject 90.7 percent of nondigit patterns while achieving a 96.4 percent correct classification of isolated test digits. When our classifier is applied to the recognition of 4958 connected handwritten digit strings (4555 2-digit, 355 3-digit, and 48 4-digit strings) from the NIST Special Database 3 with a dynamic programming approach, it has a correct classification rate of 82.4 percent with a rejection rate of as low as 0.85 percent. Our classifier compares favorably in terms of correct classification rate and robustness with other classifiers that are tested.
Image classification with structured self-organization map

Adaptive processing of structured data using a supervised learning scheme has been successfully applied to many domains, e.g., molecular biology, image classification and retrieval. A self organizing map (SOM) type algorithm for processing of structured data using an unsupervised learning approach has previously been proposed. We present an approach using quadtree representation to extract an image structure, and the application of such a structured SOM to image classification problems. Encouraging results achieved by using only six simple visual features show that the structured SOM works well for structural information.
Hierarchical content classification and script determination for automatic document image processing

Page segmentation and image content classification plays an important role in automatic document image processing with applications to mixed-type document image compression, form and check reading, and automatic mail sorting. We propose an enhanced background-thinning based page segmentation algorithm to process document images rapidly and eliminate some small regions embedded in other regions. We then present a hierarchical approach, which combines the cross correlation measure, Kolmogorov complexity measure, and a neural network, to classify sub-images into halftones and texts. The approach also achieves high accuracy in text determination using a three-layer feed-forward network where the text region can be classified into Chinese or alphabetic characters. Experimental results on a number of mixed-type document images show the efficiency and effectiveness of our approach.
Fuzzy integral for leaf image retrieval

Generally, the more features utilized, the better the retrieval performance. However, it is a very challenging task to combine different feature sets in a way reflecting human perception. This paper presents the combination of different shape based feature sets using fuzzy integral for leaf image retrieval. The feature sets used in our system include centroid-contour distance curve, eccentricity, and angle code histogram. The fuzzy integral approach can release the user's burden from tuning the combination parameters. In order to reduce the matching time in the retrieval process, a thinning based method is proposed to locate the start point of a leaf contour. Experimental results on 440 leaf images from 44 plant species (10 samples from each plant species) show that the fuzzy integral approach can achieve a comparable retrieval performance with the best case of the weighted summation combination. The results also indicate that our approach, which are more efficient, can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method.
Image Thresholding by Maximizing the Index of Nonfuzziness of the 2-D Grayscale Histogram

Image segmentation plays an important role in various image processing applications including robot vision and document image analysis and understanding. In contrast to classical set theory, fuzzy set theory, which takes into account the uncertainty intrinsic to various images, has found great success in the area of image thresholding. In this paper, an image thresholding approach based on the index of nonfuzziness maximization of the 2-D grayscale histogram is proposed. The threshold Vector (T, S), where T is a threshold for pixel intensity and S is another threshold for the local average of pixels, is obtained by an exhaustive searching algorithm. In this approach, the difference between these two components (T and S) is guaranteed to be within a relatively small range, which leads to reasonable results from the viewpoint of human vision perception. This cannot be achieved in certain entropy-based methods. Experimental results have shown that our proposed approach not only performs well and effectively but also is more robust when applied to noisy images.
A fast 2D entropic thresholding method by wavelet decomposition

Compared with ID grayscale histogram analysis, 2D entropic thresholding makes use of local average as well as pixel gray level. However, it is time consuming to search the threshold vector in the 2D histogram. In the paper, a fast algorithm using wavelet decomposition is proposed, with which a set of candidates of the vector was first obtained in the decomposed histogram. The optimal threshold vector is then obtained without exhaustive searching. Experimental results have shown that our algorithm not only finds the threshold vector as well as Brink's method (1992) but also saves computation costs, using up only 0.53% of the processing time taken by exhaustive searching.
Script determination of mixed Chinese/English document images using Kolmogorov complexity measure

In this paper, we propose an approach based on Kolmogorov Complexity (KC) measuie for determining script classes in mixed Chinese (complex characters)/English document images. This approach, which mainly consists of two steps: document image preprocessing and KC measure, can successfully separate Chinese text lines from English ones. Our approach is robust and reliable in handling document images of different appearances and densities, and various fonts, sizes and styles of characters used in documents. Experimental results on a set of 40 text line images (20 English text lines and 20 Complex Chinese text lines) from various document images show that 100% correct classification rate can be achieved.
Segmentation and recognition of on-line Pitman shorthand outlines using neural networks

This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6% by using our approach.
Edge detection from edge knots in live plant image processing

This paper proposes an optimization model for extracting edges in gray-scale images. The model sufficiently utilizes the gray-level information in a pair of orthogonal directions at each considered pixel. The model has three major features in its novelty: (1) Emphasizing the globality of traditional local features; (2) Being a generalized case of the classical snake models; and (3) Offering a theoretical interpretation to the setting of the parameters for the method based on the Simulation of Particle Motion in a Vector image Field (SPMVIF). Our Edge Detection from Edge Knots (EDEK) model can be divided into two stages: the generation ofedge knots on or near edges and a propagation process for producing complete edges from these edge knots. One advantage ofour approach is that the propagation process does not depend on any control parameters. The EDEK model is suitable for live plant image processing, which is demonstrated by a number of simulation results on the edge detection of live plant images. Our model is simple in computing and robust, and can perform very well even in situations where high curvature exists.
A fuzzy image metric with application to fractal coding

Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L(2)-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with the human visual perception in many cases. A fuzzy image metric (FIM) is defined based on Sugeno's (1977) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgment procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using the PSNR. Compared to the L(2)-metric, we demonstrate that a better performance can be achieved in fractal coding by using the proposed FIM.
On the maximization of the crispness of 2D grayscale histogram for image thresholding

In this paper, a novel image thresholding approach based on the crispness maximization of the 2D grayscale histogram is proposed. The threshold vector (T,S) is obtained by an exhaustive search algorithm. In this approach, the difference between these two components is guaranteed to be within a relatively small range. This cannot be achieved in fuzzy entropy based methods, such as Abutaleb, Brink's method. Experimental results show that our proposed approach not only performs well and effectively but also is more robust when applied to noisy images.
Solving linear simultaneous equations by constraining learning neural networks

This paper proposes using constrained learning algorithm (CLA) to solve linear equations, where the corresponding conrtraint relations for this problem is just the linear equations. As a result. the CLA can be effectively and appropriately applied It was found in experiments that the convergent speedfor this CLA is much faster than the recursive least square back propagation (RLS-BP) algorithm. Finally, related experimental results are presented.
Handwritten Chinese character segmentation using a two-stage approach

Correct segmentation of handwritten Chinese characters is crucial to the successful recognition. However, because of the many difficulties involved, little work has been done in this area. In this paper, a two-stage approach is addressed to segment unconstrained handwritten Chinese character strings. A string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. At the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. Geometric features are extracted and fuzzy decision rules learned from examples are used to evaluate the segmentation paths. By using this two-stage segmentation approach, we can achieve both good performance and efficiency in segmenting unconstrained handwritten Chinese characters.
Match between normalization schemes and feature sets for handwritten Chinese character recognition

Because of the large number of Chinese characters and many different writing styles involved, the recognition of handwritten Chinese characters remains a very challenging task. It is well recognized that a good feature set plays a key role in a successful recognition system. Shape normalization is as well an essential step toward achieving translation, scale, and rotation invariance in recognition. Many shape normalization methods and different feature sets have been proposed in the literature. We first review five commonly used shape normalization schemes and then discuss various feature extraction techniques usually used in handwritten Chinese character recognition. Based on numerous experiments conducted on 3,755 handwritten Chinese characters (GB2312-80), we discuss the matches made between the normalization schemes and the feature sets and suggest the best match between them in terms of classification performance. The nearest neighbor classifier was adopted in our experiments with templates obtained by using the K-means clustering algorithm.
A fuzzy metric for image quality assessment

Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L/sup 2/-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with human visual perception in many cases. In this paper, a fuzzy image metric (FIM) is defined based on M. Sugeno's (1974) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgement procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using PSNR.
Adaptive Processing of Tree-Structure Image Representation

Much research on image analysis and processing has been carried out for the last few decades. However, it is still challenging to represent the image contents effectively and satisfactorily. In this paper, a segmentation-free tree-structure image representation is presented. In order to learn the structure representation, a back-propagation through structure (BPTS) algorithm is adopted. Experiments on plant image classification and retrieval refining using only six visual features were conducted on a plant image database and a natural scene image database, respectively. Encouraging results have been achieved.
Neural networks with problem decomposition for finding real roots of polynomials

This paper proposes applying feedforward neural networks (FNN) with problem decomposition and constrained learning to finding the real roots of polynomials. In order to alleviate ihe load of the computational complexity for high order polynomials, this network model is extended to one which works recursively with a small number of the real roots of a polynomial (less than the total number of roots to be found) obtained at a time. The recursive formulae for finding i real roots at a time are presented Finallx some computer simulaiion results are reported.
Finding complex roots of polynomials by feedforward neural networks

This paper extended the finding real roots of polynomials by feedforward neural nehvorks (FNN) with constrained learning algorithm (CLA) to finding complex roots of arbitrary polynomials (including complex ones). Likewise, for those high order complex polynomials, this network model is also extended to one which fin& recursively a small number of complex roots af a polynomial (less than the total number of roots lo be found) at a time. Finalb some experimental results are presented.
ART2 Neural Network for Surface EMG Decomposition

Extraction of individual Motor Unit Action Potentials (MUAPs) from a surface ElectroMyoGram (EMG) is an essential but challenging task for clinical study and physiological investigation. This paper presents an automatic decomposition of surface EMGs using a self-organised ART2 neural network. In our approach, MUAP peaks are first detected using a Weighted Low-Pass Differential (WLPD) filter. A modified ART2 network is then utilised to classify MUAPs based on MUAP waveforms and firing time information. Individual MUAP trains are identified from real surface EMG signals recorded during weak contraction, and also from simulated surface EMGs. The firing statistics and the waveforms of individual MUAPs are then extracted. A number of computer tests on 50 simulated and real surface EMGs of limb muscles show that up to five MUAP trains can be effectively extracted, with their waveforms and firing parameters estimated. Being able to decompose real surface EMGs has essentially demonstrated the potential applications of our approach to the non-invasive diagnosis of neuromuscular disorders.
A Semi-Parametric Hybrid Neural Model for Nonlinear Blind Signal Separation

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.
Ranking Segmentation Paths Using Fuzzified Decision Rules

Connected characters are usually recognized using a segmentation-based approach in which individual characters are segmented first by segmentation paths before they are fed to a character classifier. Good ranking of segmentation paths can significantly shorten the processing time in evaluating segmentation paths and improve the recognition rate by avoiding misinterpretation. In this chapter, we present a method of using fuzzified decision rules to rank segmentation paths. The fuzzified decision rules are obtained by fuzzifying the decision rules extracted from a decision tree learned from examples. Nine measures of the properties of a segmentation path are extracted as the input and the centroid defuzzification method is adopted to defuzzify the output. Our method of ranking segmentation paths has been tested on the NIST Special Database 3 with good results.
HMMRF: a stochastic model for offline handwritten Chinese character recognition

This paper proposes a hidden Markov mesh random field (HMMRF)-based stochastic model for off-line handwritten Chinese characters recognition using statistical observation sequences embedded in the strokes of a character. Due to the great amount of Chinese characters and many different writing styles or variations, the recognition of handwritten Chinese characters becomes more difficult and challenging than any other character recognition. In our approach, a new framework based on HMMRF model is put forward at first. The estimation of model parameters and state sequence decoding algorithms are also discussed later. Besides the mathematical model and corresponding issues, nonlinear shape normalization scheme that modifies the distortion and adjusts the correlation of strokes is applied. Two types of stroke-based features are extracted for the rough classification and observation sequence respectively. Experimental results on isolated handwritten Chinese characters demonstrate the effectiveness of our approach.
A hierarchical distributed genetic algorithm for image segmentation

A novel hierarchical distributed genetic algorithm is proposed for image segmentation. Firstly, a technique of histogram dichotomy is proposed to explore the statistical property of input image and produce a hierarchical quantization image. Then a hierarchical distributed genetic algorithm (HDGA) is imposed on the quantized image to explore the spatial connectivity and produce final segmentation result. HDGA is a major improvement of the original distributed genetic algorithm (DGA) and multiscale distributed genetic algorithm (MDGA) in four aspects: (1) HDGA does not require the a priori number of image regions, however it can effectively and adaptively control the segmentation quality; (2) the chromosome structure is revised from the original label (multilabel)-condition-fitness format to a more compact (storage-efficient) label-fitness format; (3) the fitness function is revised to utilize the spatial connectivity, but not the original "reconstruction" error; (4) three revised genetic operations are presented to make the algorithm computation-efficient. Our experiments give proofs for the advantages of HDGA.