nils-herrmann/clean_scirepeval huggingface dataset:
A New Method for Rotation Free Method for Online Unconstrained Handwritten Chinese Word Recognition: A Holistic Approach

Most online handwriting word recognition (HWR) approaches proceed by segmenting words into isolate characters which are recognized separately. Inspired by results in cognitive psychology, holistic word recognition approaches provides another effective way to deal the problem of HWR. In this paper, we propose a new method for rotation free online unconstrained Chinese word recognition through a holistic approach. By a gravity center balancing skew detection and correction method, the rotation ranging from 0° to 360° of a Chinese handwritten word can be detected. Through the process of preprocessing, feature extraction using elastic meshing technique and classification, the handwritten words with characters even connected or partially overlapped can be recognized through a holistic approach. Experiments were performed on 8888 categories of 1,137,664 unconstrained handwritten Chinese word samples. Experimental results for randomly rotated unconstrained cursive handwritten Chinese word data demonstrated that the proposed method can achieve about 96.58% recognition accuracy.
Online Heterogeneous Feature Fusion for Visual Recognition

Heterogeneous Feature Fusion Machines (HFFM) is a kernel based logistic regression model which effectively fuses multiple features for visual recognition tasks. However, its batch mode solution suffers inefficiency and poor scalability as common batch algorithm does. In this paper, we developed a novel algorithm based on multiple kernels and group LASSO technique to solve this model, called online HFFM (OLHFFM). The power of the proposed scheme is demonstrated by experiments delivered on public dataset. Moreover, OLHFFM has demonstrated advantages over current state-of-the-art approach as ILK and NORMA in large-scale visual classification.
Combined Segmentation and Recognition of Online Handwritten Diagrams with High Order Markov Random Field

The recognition of online handwritten flowcharts is studied in this work. They are considered as a 2D language with two kinds of primitives: symbols and structural relationships. Three main steps are involved to process this data. They concern i) symbol hypothesis generation, ii) symbol recognition and iii) layout interpretation using grammar description to structure information at the document level. We propose to build a high order Markov random field on stroke level to cope with segmentation and recognition simultaneously. The potential function in our Markov random field is log-linear, and we trained it using max-margin method. We tested our method on two public handwritten diagram datasets and experiments show that our symbol recognition method's performance has reached state-of-the-art.
SCUT-COUCH Textline_NU: An Unconstrained Online Handwritten Chinese Text Lines Dataset

An unconstrained online handwritten Chinese text lines dataset, SCUT-COUCH Textline_NU, a subset of SCUT-COUCH  , is built to facilitate the research of unconstrained online Chinese text recognition. Texts for hand copying are sampled from China Daily corpus with a stratified random manner. The current vision of SCUT-COUCH Textline_NU has 8,809 text lines (4,813 lines are collected by touch screen LCD and 3,996 by digital pen) and 159,866 characters in total that are written by more than 157 participants. To demonstrate that the dataset is practical, an over-segmentation, dynamic programming and semantic model based algorithm was presented for segmenting and recognizing the unconstrained online Chinese text lines. In preliminary experiments on the dataset, the proposed algorithm recognition achieves a baseline accuracy of 56.41%.
High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps

Just like its great success in solving many computer vision problems, the convolutional neural networks (CNN) provided new end-to-end approach to handwritten Chinese character recognition (HCCR) with very promising results in recent years. However, previous CNNs so far proposed for HCCR were neither deep enough nor slim enough. We show in this paper that, a deeper architecture can benefit HCCR a lot to achieve higher performance, meanwhile can be designed with less parameters. We also show that the traditional feature extraction methods, such as Gabor or gradient feature maps, are still useful for enhancing the performance of CNN. We design a streamlined version of GoogLeNet [13], which was original proposed for image classification in recent years with very deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we used is 19 layers deep but involves with only 7.26 million parameters. Experiments were conducted using the ICDAR 2013 offline HCCR competition dataset. It has been shown that with the proper incorporation with traditional directional feature maps, the proposed single and ensemble HCCR-GoogLeNet models achieve new state of the art recognition accuracy of 96.35% and 96.74%, respectively, outperforming previous best result with significant gap.
Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

Writer adaptation has been proved to be an effective approach to improve the recognition performance of the writer-independent recognizer for a particular writer. In this paper, we propose a writer adaptive handwriting recognition approach by incremental learning the Modified Quadratic Discriminant Function (MQDF) classifier. We derived the solution of Incremental MQDF (IMQDF) and then present a Discriminative IMQDF (DIMQDF) by deriving the solution of IMQDF in the updated discriminative feature space. Based on IMQDF or DIMQDF, the writer adaptation is finally performed by updating the MQDF recognizer adaptively. The experimental results for recognizing handwriting Chinese characters indicate that the proposed IMQDF and DIQMDF approaches can reduce as much as 52.71% and 45.38% error rate respectively on the writer-dependent dataset while only have less than 0.18% accuracy loss on the writer-independent dataset. In other words, the proposed IMQDF and DIMQDF based writer adaptation approaches can significantly increase the recognition accuracy on writer-dependent dataset while only have limited negative influence for general writer.
YOLSE: Egocentric Fingertip Detection from Single RGB Images

With the development of wearable device and augmented reality (AR), the human device interaction in egocentric vision, especially the hand gesture based interaction, has attracted lots of attention among computer vision researchers. In this paper, we build a new dataset named EgoGesture and propose a heatmap-based solution for fingertip detection. Firstly, we discuss the dataset collection detail and as well the comprehensive analysis of this dataset, which shows that the dataset covers substantial data samples in various environments and dynamic hand shapes. Furthermore, we propose a heatmap-based FCN (Fully Convolution Network) named YOLSE (You Only Look what You Should See) for fingertip detection in the egocentric vision from single RGB image. The fingermap is the proposed new probabilistic representation for the multiple fingertip detection, which not only shows the location of fingertip but also indicates whether the fingertip is visible. Comparing with state-of-the-art fingertip detection algorithms, our framework performs the best with limited dependence on the hand detection result. In our experiments, we achieve the fingertip detection error at about 3.69 pixels in 640px x 480px video frame and the average forward time of the YOLSE is about 15.15 ms.
A Novel Approach for Rotation Free Online Handwritten Chinese Character Recognition

This paper presents a method for rotation free online handwritten Chinese character recognition (RFOHCCR). Given a skew online handwritten character sample, two orientation correction steps, including angle rectification according to the starting point, angle readjustment based on principal direction axes, are first performed to rectify the skew angle of the sample. Then 8-directional feature is extracted and the character is classified using the classifier trained by artificially rotated samples. Experiments on 863 online Chinese character dataset and SCUT-COUCH dataset show the effectiveness of the proposed approach.
Building Compact MQDF Classifier for Off-line Handwritten Chinese Characters by Subspace Distribution Sharing

Quadratic classifier with modified quadratic discriminant function (MQDF) has been successfully applied to recognition of handwritten characters to achieve very good performance. However, for large category classification problem such as Chinese character recognition, the storage of the parameters for the MQDF classifier is huge which makes it not practical to be used in hand-held devices. In this paper, we propose a method to build a compact MQDF classifier for handwritten Chinese characters. We use subspace distribution sharing to form a codebook for the subspaces of the MQDF classifier's parameters. Further more, we employ a two-level Euclidean distance classifier to accelerate the recognition process. Fast recognition speed and compact dictionary size make the high accuracy quadratic classifier be practical for hand-held devices.
Chinese character-level writer identification using path signature feature, DropStroke and deep CNN

Most existing online writer-identification systems require that the text content is supplied in advance and rely on separately designed features and classifiers. The identifications are based on lines of text, entire paragraphs, or entire documents; however, these materials are not always available. In this paper, we introduce a path-signature feature to an end-to-end text-independent writer-identification system with a deep convolutional neural network (DCNN). Because deep models require a considerable amount of data to achieve good performance, we propose a data-augmentation method named DropStroke to enrich personal handwriting. Experiments were conducted on online handwritten Chinese characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes from 420 writers. For each writer, we only used 200 samples for training and the remaining 3,666 for testing. The results reveal that the path-signature feature is useful for writer identification, and the proposed DropStroke technique enhances the generalization and significantly improves performance.
A comparative study of gabor feature and gradient feature for handwritten chinese character recognition

Gabor feature and gradient feature have been proven to be two most efficient features for handwritten character recognition recently. However, few comprehensive comparative researches on the performance of these two methods in large scale handwritten Chinese character recognition (HCCR) were reported in the literature. In this paper, we compare these two methods for large scale HCCR. Some new interesting conclusions were obtained through this study. The result showed that the performance of gradient feature significantly outperforms gabor feature. Multi-channel Gabor feature can improve the performance of single-channel gabor feature. We also observed that the recognition accuracy hardly get obvious increasing after the number of directions of gabor feature achieves 5. That means only 5 directional parameters of gabor features are necessary to achieve a good enough performance, rather than 8 directions which are widely used in the literature.
Synthesis of Chinese Character Using Affine Transformation

We present a novel Chinese synthesis method based on affine transform. A set of basic Chinese character element (BCCE) are designed, which can be used to generate any Chinese character in standard GB2312-80 level 1. Structure similarity measurement is used to evaluate the synthesis quality. Experiments showed that the synthesized characters look smooth and natural. Storage of synthesized characters can be greatly reduced. The proposed Chinese character synthesis method has many potential applications, such as building small-size Chinese font, building compact classifier for Chinese OCR, and etc.
Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification. LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification. Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).
One stroke cursive character recognition using combination of directional and positional features

The paper proposes a new hybrid approach of directional and positional features for on-line one stroke cursive character recognition based on a dynamic time warping (DTW) algorithm. In our camera based user interface, a user inputs various kinds of characters, including Chinese characters, by moving a fingertip. All strokes of the character are connected, so our recognizer is designed for one stroke cursive character recognition. A quadratic curve equation for a local distance measure is employed in DTW to improve the robustness of the classifier, especially for complicated characters. By reconstructing a positional feature from a directional feature, only directional vectors need to be recorded. Thus, the template file size can be reduced a lot. As the template size is small (about 300 K including Chinese characters) and the templates can be easily customized by a user, the recognizer is suitable for hand-held devices. The efficiency of our approach is demonstrated by the promising experimental results.
Performance analysis and framework optimization of open source cloud storage system

More and more embedded devices, such as mobile phones, tablet PCs and laptops, are used in every field, so huge files need to be stored or backed up into cloud storage. Optimizing the performance of cloud storage is very important for Internet development. This paper presents the performance evaluation of the open source distributed storage system, a highly available, distributed, eventually consistent object/blob store from OpenStack cloud computing components. This paper mainly focuses on the mechanism of cloud storage as well as the optimization methods to process different sized files. This work provides two major contributions through comprehensive performance evaluations. First, it provides different configurations for OpenStack Swift system and an analysis of how every component affects the performance. Second, it presents the detailed optimization methods to improve the performance in processing different sized files. The experimental results show that our method improves the performance and the structure. We give the methods to optimize the object-based cloud storage system to deploy the readily available storage system.
Hybrid recognition for one stroke style cursive handwriting characters

Online handwriting recognition has continued to persist as a popular research field while pen computing applications are widely used in recent years. This paper proposes a novel hybrid system for one stroke style cursive handwriting character recognition. In the system, user can use fingertip to write various kinds of virtual characters (represented by trajectory of fingertip) such as alpha-numeric characters and Chinese characters through a digital camera based user interface. Without pen-up and pen-down information, the virtual characters are written in one stroke. An online and an offline recognition method for such kind of cursive characters are proposed. A hybrid approach of these two methods is proposed to combine the advantages of both of them. Benefit from the integration, the recognition accuracy was increased from 80.6% (offline classifier) and 83.4% (online classifier) to 90.9% (integrated) for stroke order free one stroke cursive handwriting Chinese characters.
Weightlessness feature — a novel feature for single tri-axial accelerometer based activity recognition

In this paper, a novel weightlessness feature for activity recognition from a tri-axial acceleration signals have been proposed. Since the orientation between accelerometer and userpsilas body may continuously change when user perform activities, we propose an algorithm to calibrate the actual vertical direction of accelerometer signal through estimating the gravitational direction. We combine peaks of signal and weightlessness feature to produce six dimensional weightlessness-based features for activity recognition. Classification of the activities is performed with Support Vector Machine (SVM). The average accuracy of four activities using the proposed weightlessness-based features is 97.21%, which are better than using traditional widely used time-domains features (mean, standard deviation, energy and correlation of acceleration data). Experimental results show that the new features can be used to effectively recognize different human activities and they are robust for different location of accelerometer.
An Improved RPCL Algorithm for Determining Clustering Number Automatically

Conventional k-means needs to know the exact cluster number before performing data clustering. Otherwise, it may lead to a poor clustering performance. The rival penalized competitive learning algorithm (RPCL) can automatically select the correct cluster number, but it is sensitive to the learning rate and the de-learning rate especially the de leaning rate. This paper presents an improved RPCL algorithm, which is based on the evaluation of competition ability between the winner and the rival, the improved RPCL algorithm could determining clustering number without the selecting of de learning rate. Our experiments have shown that this improved algorithm can find out the correct clustering number more quickly and convenient than RPCL algorithm
Activity recognition from acceleration data based on discrete consine transform and SVM

This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.
A novel orientation free method for online unconstrained cursive handwritten chinese word recognition

In this paper, we propose an orientation free method for unconstrained cursive handwritten chinese word recognition. By a novel gravity center balancing method, the orientation of a handwritten word can be detected. Through the process of stroke extraction, stroke breaking, heuristic over-segmentation and path searching by recognition and lexicon information, the handwritten word with characters even connected or partially overlapped can be recognized. Experiments were performed on 173,660 unconstrained handwritten Chinese word samples collected by Pocket PC. The promising experimental results demonstrated our method is an orientation free and stroke-order free method for unconstrained cursive handwritten Chinese word recognition.
A novel method for evaluating facial attractiveness

Beauty is an abstract concept that is inherently difficult to quantify and evaluate. The analysis of facial attractiveness has received much research attention in the past. Recent work has shown that facial attractiveness can be learned by machine, using supervised learning techniques. This paper proposes a computational method for estimating facial attractiveness based on Gabor features and support vector machine (SVM). We conducted several experiments using different feature types including Gabor features, geometric features, and eigenfaces. We found that the Gabor feature-based method produced the best result. To further improve the performance of this predictor, we combined Gabor features with geometric facial features, and a high correlation of 0.93 with average human ratings was achieved. This result indicates that our new approach performs well in the evaluation of facial attractiveness.
A Static Candidates Generation Technique and its Application in Two-stage LDA Chinese Character Recognition

As an effective tool for feature selection, liner discriminate analysis (LDA) has been widely used in the field of Chinese Character Recognition. In this paper, we propose a novel static candidates generation technique, which significantly reduces the storage and the computational complexity of the traditional LDA. Using the proposed technique, a two-stage LDA recognition scheme for Chinese character recognition is presented. Compared with minimum distance classifier and LDA plus minimum distance classifier, the error ratio of proposed scheme significantly decline 60% and 35% respectively, which shows the validity of the proposed approach.
HRPaaS: A Handwriting Recognition Platform as a Service  Based on Middleware and the HTTP API

In this paper, we present a new Handwriting Recognition Platform as a Service (HRPaaS) using cloud infrastructure as a service(IaaS), middleware, and HTTP application programming interface (API) technologies. Based on the OpenStack cloud IaaS architecture, a handwriting recognition HTTPAPI service is designed to provide a highly reliable, real-time, customized handwriting character recognition service for application developers. Using middleware and cloud computing technologies, the computing resources, HTTPAPI response service pool, and backend handwriting recognition service pool can be loosely coupled and integrated to achieve fast and flexible deployment of the HRPaaS.
DeepWriterID: An End-to-End Online Text-Independent Writer Identification System

The rapid adoption of touchscreen mobile terminals and pen-based interfaces has increased the demand for handwriting-based writer identification systems, particularly in the areas personal authentication and digital forensics. However, most writer identification systems yield poor performance because of insufficient data and an inability to handle the various conditions inherent in handwriting samples. To address these problems, the authors introduce the end-to-end DeepWriterID system that employs a deep convolutional neural network (CNN) and incorporates a new method called DropSegment to achieve data augmentation and improve the generalized applicability of CNN. Experiments show DeepWriterID achieves accuracy rates of 95.72 percent for Chinese text and 98.51 percent for English text.