nils-herrmann/clean_scirepeval huggingface dataset:
LPSNet: A Novel Log Path Signature Feature Based Hand Gesture Recognition Framework

Hand gesture recognition is gaining more attentions because it's a natural and intuitive mode of human computer interaction. Hand gesture recognition still faces great challenges for the real-world applications due to the gesture variance and individual difference. In this paper, we propose the LPSNet, an end-to-end deep neural network based hand gesture recognition framework with novel log path signature features. We pioneer a robust feature, path signature (PS) and its compressed version, log path signature (LPS) to extract effective feature of hand gestures. Also, we present a new method based on PS and LPS to effectively combine RGB and depth videos. Further, we propose a statistical method, DropFrame, to enlarge the data set and increase its diversity. By testing on a well-known public dataset, Sheffield Kinect Gesture (SKIG), our method achieves classification rate as 96.7% (only use RGB videos) and 98.7% (combining RGB and Depth videos), which is the best result comparing with state-of-the-art methods.
A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine

Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET. With hybrid deep structure, the proposed network is able to learn complex spatial and temporal patterns from single-trial ERP signals. To verify the effectiveness of ERP-NET, we carried out a few ERP detection experiments that the proposed model achieved cutting-edge performance. The experimental results demonstrate that the patterns learned by the ERP-NET are discriminative ERP components in which the ERP signals are properly characterized. More importantly, as an effective approach to single-trial analysis, ERP-NET is able to discover new ERP patterns which are significant to neuroscience study as well as BCI applications. Therefore, the proposed ERP-NET is a promising tool for the research on ERP signals.
A New Rotation Feature for Single Tri-axial Accelerometer Based 3D Spatial Handwritten Digit Recognition

A new rotation feature extracted from tri-axial acceleration signals for 3D spatial handwritten digit recognition is proposed. The feature can effectively express the clockwise and anti-clockwise direction changes of the users’ movement while writing in a 3D space. Based on the rotation feature, an algorithm for 3D spatial handwritten digit recognition is presented. First, the rotation feature of the handwritten digit is extracted and coded. Then, the normalized edit distance between the digit and class model is computed. Finally, classification is performed using Support Vector Machine (SVM). The proposed approach outperforms time-domain features with a 22.12% accuracy improvement, peak-valley features with a 12.03% accuracy improvement, and FFT features with a 3.24% accuracy improvement, respectively. Experimental results show that the proposed approach is effective.
Hyperparameter search for deep convolutional neural network using effect factors

Learning a deep architecture involves a tough issue called hyperparameter search. This is especially the case for convolutional neural networks with a large number of hyperparameters. To solve this problem, we propose a tensor completion method to predict the best architecture configurations for convolutional neural networks. This method is based on a hypothesis that the generalization performance of a deep architecture is controlled by several effect factors, each of which is a function of hyperparameter of the deep architecture. Predicted generalization accuracy of the best configurations are checked by carrying out deep learning computation. Since generalization performance for a practical recognition task is always data- and code-dependent, we tried out our method on an open deep learning platform named Caffe, and we increased the generalization accuracy from 98.97% to around 99.25% on MNIST by replacing only five numbers.
Learning deep neural network using max-margin minimum classification error

Deep neural networks (DNNs) have recently achieved state-of-the-art performance on various tasks, such as image classification, handwriting recognition, text spotting, and speech recognition. Most of these DNNs use softmax regression and cross-entropy loss to calculate the loss function for optimization. However, the loss function is merely expected to raise the output of the true class and reduce others without distinction. In this paper, we propose a new max-margin minimum classification error (M3CE) training method, which is inspired by the traditional minimum classification error, but is more appropriate for training DNNs. The proposed M3CE aims not only to increase the posteriori of the true class but also to decrease the output of the most confused class, which can cover any shortage of the cross-entropy loss. We evaluate the M3CE on two popular datasets, MNIST and CIFAR-10. Experimental results show that the M3CE complements cross-entropy efficiently and achieves better performance.
Finger-writing-in-the-air system using Kinect sensor

A novel Kinect based finger-writing character recognition system called finger-writing-in-the-air is proposed in this paper. This system allows people to input characters by writing in the air virtually. We propose a mixture model for hand segmentation, which takes advantage of depth, color, and motion information. This model helps overcome problems arisen with traditional vision-based methods like illumination variation or hand-face overlapping, also solves the fatal color-depth mismatch problem accompanied by Kinect. A dual-mode switching algorithm is presented for accurate fingertip detection. It is less sensitive to noise along the segmented hand contour and can handle various hand poses. The positions of fingertip are linked and reconstructed as inkless character strokes. The recovered trajectory is recognized by the state-of-the-art handwriting character recognition method. Experiments show that user can write freely in the air inputting Chinese characters, English letters (upper and lower case) and digits in real time with over 90% accuracy rate for the first five candidates.
A Simplified Non-Gaussian Mixture Model for Signal LO Detection in a-Stable Interference

Non-Gaussian statistic model, alpha stable distribution has gained much attention due to its generality to represent heavy-tailed and impulsive interference. Unfortunately, there is no closed form expression for the probability density function of alpha-stable distributions. Hereby, to develop the approximate expressions is of importance for signal detection and denoising. This paper is concerned with the weak signal detection in α-stable interference. We present a novel approximate expression that is a simplified version of Cauchy-Gaussian mixture (CGM) for symmetric α-stable (SαS) distribution. Consequently, the non-linearity limiter of locally optimal (LO) detector is deduced. Compared with Cauchy detector, the proposed detector has near optimal performance.
Gesture recognition based on 3D accelerometer for cell phones interaction

This paper proposes a gesture recognition system based on single tri-axis accelerometer mounted on a cell phone for human computer interaction (HCI). Three feature extraction methods, namely discrete cosine transform (DCT), fast Fourier transform (FFT) and a hybrid approach which combine wavelet packet decomposition (WPD) with fast Fourier transform are proposed. Recognition of the gestures is performed with support vector machine (SVM). Recognition results are based on acceleration data collect from 67 subjects. The best average recognition result (87.36%) for 17 complex gestures is achieved with wavelet-based method, while DCT and FFT produce accuracy of 85.16% and 86.92% respectively. The performance of experimental results shows that gesture-based interaction can be used as a novel HCI for mobile applications, such as games control and music navigation.
A new stroke-based directional feature extraction approach for handwritten Chinese character recognition

A directional feature extraction approach based on stroke directional decomposition of a Chinese character is proposed. Without extracting the skeleton or contour of the character, the four directional sub-patterns, namely, horizontal (-), vertical (|), left up diagonal (/) and right up diagonal () sub-patterns could be obtained directly from analyzing the stroke directional characteristics of the character. Five kinds of line-density based elastic meshing methods are presented to extract cellular directional features. Experimentation on a total of 18800 handwritten samples from 940 categories produces a recognition rate of 92.71%, showing the effectiveness of the proposed approach.
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MC-FCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.50 and 96.58 percent, respectively, which are significantly better than the best result reported thus far in the literature.
Car plate number characters recognition using Gabor orientation features and neural networks

A new plate number character recognition method based on Gabor orientation feature and neural networks technologies is presented in this paper. Based on elastic meshing techniques and Gabor filter, a new feature extraction approach for low resolution plate number characters is proposed. An integrated neural networks model is designed as an intelligent classifier. Experiments on large data set (1000 sets of training data and 500 sets of testing data) shows the recognition rate of 99.26%, suggesting that the proposed approach is very effective.
Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition

With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition.
Identifying Machine-Printed and Handwritten Texts Using DropRegion and Deep Convolutional Network

In this paper, we propose a deep convolutional neural network to identify machine-printed and handwritten texts. We also propose a novel data augmentation technique called DropRegion to make up for the lack of available data and enhance the generalization of the model. DropRegion increases data diversity by randomly dropping one of the stroke-containing regions in each raw input text-line image. Two parameters are introduced to make DropRegion adjustable for different data. For distinguishing texts of mixture of five languages including English, Chinese, Japanese, Korean and Russian, we have successfully achieved a very promising accuracy of 99.07% after DropRegion is applied, which is a significantly better performance compared to traditional method (97.91%) and our deep convolutional network baseline (98.75%).
Activity recognition from acceleration data using AR model representation and SVM

In this paper, the autoregressive (AR) model of time-series is presented to recognize human activity from a tri-axial accelerometer data. Four orders of autoregressive model for accelerometer data is built and the AR coefficients are extracted as features for activity recognition. Classification of the human activities is performed with support vector machine (SVM). The average recognition results for four activities (running, still, jumping and walking) using the proposed AR-based features are 92.25%, which are better than using traditional frequently used time domains features (mean, standard deviation, energy and correlation of acceleration data) and FFT features. The results show that AR coefficients obvious discriminate different human activities and it can be extract as an effective feature for the recognition of accelerometer date.
Finger-Written Chinese Characters Recognition Using Hierarchical LDA

In our camera based user interface, user inputs Chinese characters by moving fingertip with his/her fingers. Therefore, we call those characters "finger-written Chinese characters" (FWCCs). To recognize FWCCS with high recognition accuracy, we propose a new method to discriminate similar FWCCs based on a two stages linear discriminant analysis (LDA) architecture. Experimental results show the proposed method is useful for the improvements of recognition rate and outperforms our previous hybrid recognition method. Comparing with MQDF-based method, the proposed method can reach similar classification performance but with much less storage requirement.
A Comprehensive Analysis of Misclassified Handwritten Chinese Character Samples by Incorporating Human Recognition

The development of convolutional neural networks (CNN) has led to revolutionary progress in the resolution of the offline handwritten Chinese character recognition (HCCR) problem. As the recognition rate on a standard offline HCCR testbed is outstanding, a few samples that remain misclassified have kindled our interest. In this paper, with the help of human recognition results, we present a comprehensive analysis of the samples misclassified by a state-of-the-art CNN model. We performed the analysis based on the top-1-votes, which are obtained from the statistical analysis of human recognition results, and derived the following conclusions: (1) the majority of samples with high top-1-votes were mis-labeled. Besides, by comparing the results of human recognition with that of CNN, some limitations of CNN that provide scope for further improvement are presented; (2) in the samples with medium top- 1-votes, it is shown that the samples with different confidence level have different characteristics. Specifically, some samples could be regarded as multi-label samples; (3) the samples with low top-1- votes are either wrongly written or written extensively in cursive style, which are difficult to match their given ground-truths; (4)the relationship between writing styles and misclassifications are also introduced in the paper. We believe this work should provide some insights and brings new clues on designing new classification methods to deal with these challenging samples.
A mixed parallel neural networks computing unit implemented in FPGA

A FPGA implementation architecture of neural network (NN) computing unit is proposed in this paper. In the computing unit, link parallelism is designed between the input layer and middle layer of NN; neuron parallelism is designed between the middle layer and output layer. Moreover, the method of hardware implementation of the sigmoid function of NN is improved and a new method for weight locating in computing unit is proposed, which can simply the implementation of the NN greatly.
SpottingNet: Learning the Similarity of Word Images with Convolutional Neural Network for Word Spotting in Handwritten Historical Documents

Word spotting is a content-based retrieval process that obtains a ranked list of word image candidates similar to the query word in digital document images. In this paper, we present a convolutional neural network (CNN) based end-to-end approach for Query-by-Example (QBE) word spotting in handwritten historical documents. The presented models enable conjointly learning the representative word image descriptors and evaluating the similarity measure between word descriptors directly from the word image, which are the two crucial factors in this task. We propose a similarity score fusion method integrated with hybrid deep-learning classifica-tion and regression models to enhance word spotting perfor-mance. In addition, we present a sample generation method using location jitter to balance similar and dissimilar image pairs and enlarge the dataset. Experiments are conducted on the George Washington (GW) dataset without involving any recognition methods or prior word category information. Our experiments show that the proposed model yields a new state-of-the-art mean average precision (mAP) of 80.03%, significantly outperforming previous results.
Recognition confidence analysis of handwritten Chinese character with CNN

In this paper, we present an effective method to analyze the recognition confidence of handwritten Chinese character, based on the softmax regression score of a high performance convolutional neural network (CNN). Through careful and thorough statistics of 827,685 testing samples that randomly selected from total 8836 different classes of Chinese characters, we find that the confidence measurement based on CNN is an useful metric to know how reliable the recognition results are. Furthermore, we find by experiments that the recognition confidence can be used to find out similar and confusable character-pairs, to check wrongly or cursively written samples, and even to discover and correct mislabeled samples. Many interesting observation and statistics are given and analyzed in this study.
A New Feature Optimization Method Based on Two-Directional 2DLDA for Handwritten Chinese Character Recognition

LDA transformation is one of the popular feature dimension reduction techniques for the feature extraction in most handwritten Chinese characters recognition systems. The integration of the feature extraction and LDA transformation can be viewed as a two-directional feature transformation procedure, one is the pixel-level feature transformation by the summing up or blurring, another is by the LDA matrix, and the transformation coefficients are set empirically in the former. In this paper, we proposed a feature optimization method based on the gradient feature extraction by using the two-directional 2DLDA, which can find the optimal transformation coefficients in two directions. A series of experiments on the randomly selected 15 groups of the similar Chinese character samples from HCL2000 have indicated that, our method can effectively improve the recognition performance, the error rate reduction reaches 45.02% comparing to the traditional method, showing the effectiveness of the proposed approach.
Enhanced visual categorization performances by incorporation of simple features into bim features

Recent studies have demonstrated that Biologically Inspired Model features (BIM) are effective for object or scene categorization. However, according to BIM's forming mechanism, it may not hit the typical pattern due to its blind feature selection. In order to provide more informative pattern information for different visual object classes, a large number of prototypes have to be used in describing images. This leads to huge redundancy which may decrease categorization accuracy. Thus, improving BIM feature's performance by just increasing the number of prototypes is not adequate. In this paper, we propose an integrated approach to address this problem. In our approach, some simple non-biological features such as color histogram and Edge Orientation Histogram (EOH) are incorporated into BIM for discriminative image representation. Experimental results have shown that combination of BIM and simple features can improve visual categorization performances significantly.
Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge

Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR). However, most current DCNN-based HCCR approaches treat the handwritten sample simply as an image bitmap, ignoring some vital domain-specific information that may be useful but that cannot be learnt by traditional networks. In this paper, we propose an enhancement of the DCNN approach to online HCCR by incorporating a variety of domain-specific knowledge, including deformation, non-linear normalization, imaginary strokes, path signature, and 8-directional features. Our contribution is twofold. First, these domain-specific technologies are investigated and integrated with a DCNN to form a composite network to achieve improved performance. Second, the resulting DCNNs with diversity in their domain knowledge are combined using a hybrid serial-parallel (HSP) strategy. Consequently, we achieve a promising accuracy of 97.20% and 96.87% on CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best results previously reported in the literature.
DeepFinger: A Cascade Convolutional Neuron Network Approach to Finger Key Point Detection in Egocentric Vision with Mobile Camera

In this paper, we introduce a new approach to finger key point detection. For RGB images captured from an egocentric vision with a mobile camera, fingertip point detection remains a challenging problem due to various factors, like background complexity, illumination variety, hand shape diversity, and image blur cause by camera movements. To address these issues, we propose a bi-level cascade structure of a convolutional neuron network (CNN). The first-level CNN generates a bounding box of hand region by filtering a large proportion of complicated background information. Using the bounding box area as input, the second-level CNN including an extra branch returns accurate fingertip location with a multi-channel dataset. Our approach is the first attempt of finger key point detection from an egocentric vision with a mobile camera. The proposed method achieves satisfying and significant better results compared to previous fingertip detection methods based on handcraft features.