nils-herrmann/clean_scirepeval huggingface dataset:
Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network

The path signature feature (PSF) which was initially introduced in rough paths theory as a branch of stochastic analysis, has recently been successfully applied to the field of pattern recognition for extracting sufficient quantity of information contained in a finite trajectory, but with potentially high dimension. In this paper, we propose a variation of path signature representation, namely the dyadic path signature feature (D-PSF), to fully characterize the trajectory using a hierarchical structure to solve the rotation-free online handwritten character recognition (OLHCR) problem. We adopt the deep neural network (DNN) as classifier, and investigate three hanging normalization methods to improve the robustness of the DNN to rotational distortions. Extensive experiments on digits, English letters, and Chinese radicals demonstrated that the proposed D-PSF, jointly with hanging normalization and DNN, achieved very promising results for rotated OLHCR, significantly outperforming previous methods.
A New Method for Facial Beauty Assessment

Beauty is an abstract concept. How to quantify and evaluate beauty has always been a major concern among people. But few people adopt the way based on image processing and pattern recognition to assess beauty. For the first time, this paper proposed a way using computer image processing and pattern recognition for beauty assessment, and proposed a method based on the beauty- related feature points and the smoothness of the face skin. And by using the method of machine learning and pattern recognition, the paper formulates a standard to evaluate the beauty. The evaluation system proves to be simple, feasible and objective with regard to various testing samples.
Dense and Tight Detection of Chinese Characters in Historical Documents: Datasets and a Recognition Guided Detector

Characters in historical documents are typically densely distributed and are difficult to localize and segment by directly applying classic proposal and regression based methods. In this paper, we propose a novel method called recognition guided detector (RGD) that achieves tight Chinese character detection in historical documents. The proposed RGD consists of two simultaneously trained convolutional neural networks: a recognition guided proposal network that provides context information of the text and a detection network that applies this information to localize each of the characters accurately. To train and test the proposed method, we established two new datasets with character-level annotations, comprising ground truth character bounding boxes and ground truth characters in each of the boxes. The data in our datasets are scanned images collected from nine different versions of Tripitaka in Han. Experimental results show that, guided by a text recognition network with a test accuracy of 97.25%, the detection network in our proposed method achieves a much higher F-score with fewer parameters under a highly constrained evaluation criterion of intersection of union (IoU) ≥ 0.7, when comparing to several state-of-the-art object detection and text detection methods. The datasets are publicly available at https://github.com/HCIILAB/TKH_MTH_Datasets_Release for non-commercial use.
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling

Currently, owing to the ubiquity of mobile devices, online handwritten Chinese character recognition (HCCR) has become one of the suitable choice for feeding input to cell phones and tablet devices. Over the past few years, larger and deeper convolutional neural networks (CNNs) have extensively been employed for improving character recognition performance. However, its substantial storage requirement is a significant obstacle in deploying such networks into portable electronic devices. To circumvent this problem, we use a novel technique called DropWeight for pruning redundant connections in the CNN architecture. It is revealed that the method not only treats streamlined architectures such as AlexNet and VGGNet well but exhibits remarkable performance for deep residual network and inception network. We also demonstrate that global pooling is a better choice for building very compact online HCCR systems. Experiments were performed on the ICDAR-2013 online HCCR competition dataset using our proposed network, and it is found that the proposed approach requires only 0.57 MB for storage, whereas state-of-the-art CNN-based methods require up to 135 MB; meanwhile the performance is decreased only by 0.91%.
Single-layer Unsupervised Feature Learning with l2 regularized sparse filtering

Patch-based Single-layer Unsupervised Feature Learning (SUFL) has been successfully applied in several tasks of computer vision. In the feature learning process, the key ingredient is how to learn a good feature mapping that connects patches to feature vectors. Among various feature mapping methods, the sparse filtering is easy to be implemented and hyper-parameter free. However, the standard sparse filtering method only considers the sparsity distribution of the learned features, ignoring the feature mapping matrix itself. This will lead to a random magnitude for mapping matrix and further weaken the generation performance. In this paper we proposed L2 regularized sparse filtering for the feature mapping in SULF. Classification experiments on three different datasets, i.e., CIFAR-10, small Norb, and subsets of CISIA-HWDB1.0 handwritten characters, show that our method has better performance comparing with the standard sparse filtering.
A Kai style contour beautification method for Chinese handwriting characters

In this paper, we propose a novel contour-based method to beautify online handwritten Chinese character to the Kai style calligraphy. According to the feature and structure of Kai style calligraphy, Bezier curve is used to sketch the user-input stroke segment contour and the corner contour. We get the whole contour path of beautified character by connecting stroke segments' contour and corners' contour end-to-end. Anti-aliasing technology is used to make the edge of the contour fine and smooth. Finally, the path-fill algorithm is adopted to fill the inner of the contour. Our system is proved to be effective and efficient. Meanwhile, users can choose any color for the contour and the inner, which is more convenient for users' design.
Hierarchical Chinese Character Database Based on Global Affine Transformation

This paper uses optimal global affine transformation (GAT) to construct hierarchical Chinese character database. GAT can automatically match the components in database and the corresponding components in characters. Therefore, GAT can avoid large labor that is needed for selecting points by hand. In order to improve the computing speed, specific expressions of the parameters are achieved and preprocessings are applied to characters' and components' images in this paper. Experimental results show that computing speed has been improved twenty times with good simulated results.
An Empirical Comparative Study of Online Handwriting Chinese Character Recognition: Simplified vs. Traditional

There are two forms of Chinese character widely used in the world, simplified Chinese and traditional Chinese. Simplified Chinese is mainly used in Chinese mainland, Singapore, Malaysia and other Southeast Asian regions, while traditional Chinese is mainly used in Hong Kong, Macao, Taiwan, Japan and so on. The simplified Chinese is transformed from traditional Chinese by simplifying the character structure and reducing the stoke number of many traditional characters, while a few remain unchanged. The simplified Chinese reduces the memory storage and makes handwriting become more convenient, while it also brings the problem that many characters are so similar that they are difficult to be recognized. In this paper, an empirical study of the handwriting simplified/traditional Chinese character recognition are carried out in order to compare the difference between these two forms of Chinese characters. The experimental results based on SCUT-Couch2009 database show that the handwriting recognition accuracy of traditional Chinese is higher than simplified Chinese, for both unchanged part and changed part. This interesting finding may bring us some cues on the issue of confusable Chinese character recognition for further study.
FaceMore: A Face Beautification Platform on the Cloud

Face More, a cloud-based face beautification platform for intelligent face manipulation, is developed in this work. It provides flexible and efficient cloud API to develop automatic or interactive face retouching applications. A web-site, www.facemore.net, is built on Face More, where user can upload images and obtain various online face beautification services. To obtain automatic inhomogeneous editing effects in a natural and efficient manner, we analyze the novel tool, called region-aware mask, from semi-supervised learning perspective. We reformulate the optimized-based model of region-aware mask using label propagation, and propose a fast approximate algorithm for mask generation, which leads to about 40% speed improvement while maintain high visual quality for face beatification. Qualitative and quantitative evaluations were performed for mask generation and face beatification. Comparisons with five representative commercial systems, including PicTreat, Portraiture, Portrait+, Meitu and Baidu Motu, illustrate the effectiveness of our system for image enhancement of facial lighting, smoothness and color.
Coefficients' co-occurrence histogram of DWFT based feature extration with ONPP for face recognition

The important step of face recognition based on subspace method is to obtain powerful features. In this paper, we propose a novel feature extraction technique based on discrete wavelet frame transform(DWFT), which capture the information about relationship between each high frequency subband and the low frequency subband of the decomposed image at the corresponding level. In the feature space, we use orthogonal neighborhood preserving profections (ONPP) algorithm to reduce dimension. experimental results show that the proposed algorithm is effective in face recognition. Comparisons with the other approachs are also provided.
Similar Handwritten Chinese Character Recognition Using Discriminative Locality Alignment Manifold Learning

The discriminant analysis for Similar Handwritten Chinese Character Recognition (SHCR) is essential for the improvement of handwritten Chinese character recognition performance. In this paper, a new manifold based subspace learning algorithm, Discriminative Locality Alignment (DLA), is introduced into SHCR. Experimental results demonstrate that DLA is consistently superior to LDA (Linear Discriminant Analysis) in terms of discriminate information extraction, dimension reduction and recognition accuracy. In addition, DLA reveals some attractive intrinsic properties for numeric calculation, e.g. it can overcome the matrix singular problem and small sample size problem in SHCR.
Hierarchical Deep Reinforcement Learning for Continuous Action Control

Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously. In the proposed algorithm, compound skills and basic skills are learned by two levels of hierarchy. In the first level of hierarchy, each basic skill is handled by its own actor, overseen by a shared basic critic. Then, in the second level of hierarchy, compound skills are learned by a meta critic by reusing basic skills. The proposed algorithm was evaluated on a Pioneer 3AT robot in three different navigation scenarios with fully observable tasks. The simulations were built in Gazebo 2 in a robot operating system Indigo environment. The results show that the proposed algorithm can learn both high performance basic skills and compound skills through the same learning process. The compound skills learned outperform those learned by a discrete action space deep reinforcement learning algorithm.
Writer Adaptive Online Handwriting Recognition Using Incremental Linear Discriminant Analysis

Writer adaptive handwriting recognition, which has potential of increasing accuracies for a particular user, is the process of converting a writer-independent recognition system to a writer-dependent one. In this paper, we provide a general incremental learning solution for linear discriminant analysis (LDA) on the basis of previous researches, and propose an Incremental LDA (ILDA) based writer adaptive online handwriting recognition method. The adaptation is performed by modifying both the prototypes and the LDA transformation matrix through ILDA algorithm. It includes: (1) modifying prototypes in original feature space; (2) updating the LDA transformation matrix; (3) projecting the updated prototypes to LDA feature space. Experiments are performed on two datasets, the writer-dependent dataset, in which the writing style is consistent with the incremental training data, and the writer-independent dataset. The results demonstrated that our proposed method can reduce as much as 46.35% error rate on the writer-dependent dataset with only 0.20% accuracy loss on the writer-independent dataset. It indicates that our proposed method can significantly increase the recognition accuracy for a particular writer while has minor effects for general writers.
A New CNN-Based Method for Multi-Directional Car License Plate Detection

This paper presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the a CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in real-time scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.
A new fast training algorithm for SVM

A fast SVM training algorithm is proposed in this paper. By integrating kernel caching, shrinking and using second order information, a fast quadric programming(QP) trainer is achieved. For traditional two-class SVM, the generalized error bound derived from statistical learning theory(SLT) is computed and minimized for the selection of parameters, with the Zoutendijk(ZQP) idea and parallel method to speed up the process. For one-class SVM, a compression criterion is proposed to search the best kernel width automatically. Experiments demonstrate that the proposed method is significantly faster than LibSVM and requires less support vectors to achieve good classification accuracy.
Curved segmentation path generation for unconstrained handwritten Chinese text lines

A new method of generating curved segmentation paths of handwritten Chinese text line is proposed. For overlapped characters, instead of background thinning, analysis of connected components in the overlap region of two neighboring characters is carried out, and a curved segmentation path is generated by connecting a series of points that separate the connected components belonging to different characters. As for touched characters, the candidate points are identified by using the histogram and skeleton information. After they are split at all the candidate points, curved segmentation paths are generated the same way as in overlapped characters. The experimental results on the unconstrained handwritten offline Chinese text lines show that our algorithm can improve the correct segmentation rate by 3.7% and valid segmentation rate 10.7%, indicating its effectiveness.
Convolutional Multi-directional Recurrent Network for Offline Handwritten Text Recognition

In this paper, we propose a new network architecture called Convolutional Multi-directional Recurrent Network (CDRN) for offline handwritten text recognition. The conventional recurrent neural network model obtains the local context from limited directions, whereas we build up the multi-directional long short-term memory (MDirLSTM) module to abstract contextual information in various directions. Moreover, we develop a shortcut connection strategy in our proposed architecture for faster yet better convergence. In cooperation with the aforementioned methods, the proposed architecture also benefits from the following properties: (1) it obtains informative features of the input directly without involving hand-crafted features and segmentation, and (2) it is an end-to-end trainable model whose components are trained conjointly. We evaluate the performance of the proposed method on two databases: IAM words and IRONOFF. Our experimental results demonstrate a significant increase in recognition performance using MDirLSTM and shortcut connections, which suggests the effectiveness of these two proposed methods.
Character proposal network for robust text extraction

Maximally stable extremal regions (MSER), which is a popular method to generate character proposals/candidates, has shown superior performance in scene text detection. However, the pixel-level operation limits its capability for handling some challenging cases (e.g., multiple connected characters, separated parts of one character and non-uniform illumination). To better tackle these cases, we design a character proposal network (CPN) by taking advantage of the high capacity and fast computing of fully convolutional network (FCN). Specifically, the network simultaneously predicts character-ness scores and refines the corresponding locations. The character-ness scores can be used for proposal ranking to reject non-character proposals and the refining process aims to obtain the more accurate locations. Furthermore, considering the situation that different characters have different aspect ratios, we propose a multi-template strategy, designing a refiner for each aspect ratio. The extensive experiments indicate our method achieves recall rates of 93.88%, 93.60% and 96.46% on ICDAR 2013, SVT and Chinese 2k datasets respectively using less than 1000 proposals, demonstrating promising performance of our character proposal network.
Region-aware scattering convolution networks for facial beauty prediction

This paper proposes a scattering convolutional network with region-aware facial attributes to obtain a mid-level representation for facial beauty prediction (FBP). Different from the previous works that only focus on the discriminative representation for prediction, this paper also considers the invariant properties of the facial representation that reduces the variances caused by the image transformations, such as rotations and translation. The proposed region-aware scattering convolution network (RegionScatNet) is based on a deep convolution network of scattering transforms (ScatNet) integrated with facial texture and shape features. It consists of three components, including: 1) Region Extraction to obtain the significant facial perception region with implicit shape features using region-aware mask; 2) Attributes Decomposition to separate the extracted region into detail and structure facial layers by a guided filter; 3) Scattering Convolution that computes the roto-translation invariant representation of facial detail and structure for FBP by cascading three-layer wavelets filters and non-linear modulus pooling. The comparisons with related deep learning-based methods illustrate the effectiveness of RegionScatNet for FBP. The evaluations with various prediction model (like SVR and Gaussian process) and with different facial variances (like rotaion) indicate the robustness of the RegionScatNet-based features.
Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection

Detecting incidental scene text is a challenging task because of multi-orientation, perspective distortion, and variation of text size, color and scale. Retrospective research has only focused on using rectangular bounding box or horizontal sliding window to localize text, which may result in redundant background noise, unnecessary overlap or even information loss. To address these issues, we propose a new Convolutional Neural Networks (CNNs) based method, named Deep Matching Prior Network (DMPNet), to detect text with tighter quadrangle. First, we use quadrilateral sliding windows in several specific intermediate convolutional layers to roughly recall the text with higher overlapping area and then a shared Monte-Carlo method is proposed for fast and accurate computing of the polygonal areas. After that, we designed a sequential protocol for relative regression which can exactly predict text with compact quadrangle. Moreover, a auxiliary smooth Ln loss is also proposed for further regressing the position of text, which has better overall performance than L2 loss and smooth L1 loss in terms of robustness and stability. The effectiveness of our approach is evaluated on a public word-level, multi-oriented scene text database, ICDAR 2015 Robust Reading Competition Challenge 4 Incidental scene text localization. The performance of our method is evaluated by using F-measure and found to be 70.64%, outperforming the existing state-of-the-art method with F-measure 63.76%.
A New Approach for Synthesis and Recognition of Large Scale Handwritten Chinese Words

Lacking of dataset is still a serious problem for researchers who study on online handwriting word recognition (HWR). In this paper, a handwritten Chinese word synthesis method is proposed for the first time to generate a large scale handwritten Chinese word dataset. The distributions of shape and position characteristics, such as aspect radio, character interval and the angle of gravity center line in each word sample of the Word8888 dataset have been estimated respectively. Based on this, we synthesize as large as 44,208 categories of 8,311,104 unconstrained handwritten Chinese word samples. To verify the validity of the synthesized dataset, a practical rotation free handwriting Chinese word recognition system is presented based on a new holistic approach. Experimental results for randomly rotated word samples demonstrate that the holistic approach can achieve 91.96% recognition accuracy, which provides evidence for the effectiveness of our method.
A Kai Style Calligraphic Beautification Method for Handwriting Chinese Character

In this paper, we propose a method that can beautify user’s handwriting to the character of Kai style calligraphy. Structural and style features of Chinese character are summarized, and a model for representing character is proposed. Bezier curve with varying width is used for approximating user-input stroke segment. Corner shapes are modeled and utilized to beautify the corner points in the stroke. Rendering rules are designed based on the character features. Also, antialiasing technology is adopted to make the edge of the contour fine and smooth. Experimental results show the effectiveness of our system on beautifying the handwriting to Kai style calligraphy.