nils-herrmann/clean_scirepeval huggingface dataset:
DeepText: A new approach for text proposal generation and text detection in natural images

In this paper, we develop a new approach called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the novel inception region proposal network (Inception-RPN), which slides an inception network with multi-scale windows over the top of convolutional feature maps and associates a set of text characteristic prior bounding boxes with each sliding position to generate high recall word region proposals. Next, we present a powerful text detection network that embeds ambiguous text category (ATC) information and multi-level region-of-interest pooling (MLRP) for text and non-text classification and accurate localization refinement. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results.
Fully convolutional recurrent network for handwritten Chinese text recognition

This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract signature features from the input pen-tip trajectory, a fully convolutional network to learn informative representation, a sequence modeling layer to make per-frame predictions on the input sequence and a transcription layer to translate the predictions into a label sequence. We also present a refined beam search method that efficiently integrates the language model to decode the FCRN and significantly improve the recognition results. We evaluate the performance of the proposed method on the test sets from the databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition competition, and both achieve state-of-the-art performance with correct rates of 96.40% and 95.00%, respectively.
Vision-based Vehicle Type Classification Using Partial Gabor Filter Bank

A vision-based vehicle type classification method using partial Gabor filter bank is present in this paper for five vehicles categorization: sedan, van, hatchback sedan, bus and van truck. To reduce the influence caused by the hues of vehicles, we extract the Gabor features from the edge image of vehicle, instead of from the grey image. Partial Gabor filter bank approach, which can save memory and computation cost significantly, is introduced and a new partial sampling method is proposed. The experimental results show that the recognition rate reaches 95.17% using partial Gabor features, illustrating the effectiveness of the proposed approach.
Consistent relaxation matching for handwritten Chinese character recognition

Due to the complexity in structure and the various distortions (translation, rotation, shifting, and deformation) in different writing styles of handwritten Chinese characters (HCCs), it is more suitable to use a structural matching algorithm for computer recognition of HCC. Relaxation matching is a powerful technique which can tolerate considerable distortion. However, most relaxation techniques so far developed for handwritten Chinese Character recognition (HCCR) are based on a probabilistic relaxation scheme. In the paper, based on local constraint of relaxation labelling and optimization theory, the authors apply a new relaxation matching technique to handwritten character recognition. From the properties of the compatibility constraints, several rules are devised to guide the design of the compatibility function, which plays an important role in the relaxation process. By parallel use of local contextual information of geometric relaxationship among strokes of two characters, the ambiguity between them can be relaxed iteratively to achieve optimal consistent matching.
A New Writing Experience: Finger Writing in the Air Using a Kinect Sensor

With the introduction of Microsoft Kinect, there has been considerable interest in creating various attractive and feasible applications in related research fields. Kinect simultaneously captures the depth and color information and provides real-time reliable 3D full-body human-pose reconstruction that essentially turns the human body into a controller. This article presents a finger-writing system that recognizes characters written in the air without the need for an extra handheld device. This application adaptively merges depth, skin, and background models for the hand segmentation to overcome the limitations of the individual models, such as hand-face overlapping problems and the depth-color nonsynchronization. The writing fingertip is detected by a new real-time dual-mode switching method. The recognition accuracy rate is greater than 90 percent for the first five candidates of Chinese characters, English characters, and numbers.
Edge-Aware Label Propagation for Mobile Facial Enhancement on the Cloud

This paper proposes a facial enhancement framework with mask generation for cloud-based mobile applications. We mathematically analyze and unify the mask generation, as well as the state-of-the-art region-aware mask and edit propagation techniques, from a graph-based semi supervised learning perspective. Then we propose a label propagation model with a new edge-aware structure and guided feature for mask generation. The limit analysis of the model leads to a fast algorithm, which reduces the intrinsic computation cost. Then we develop a flexible and efficient cloud-based PaaS system, called FaceMore, for intelligent mobile face enhancement. The flexibility and extendibility of the cloud-based architecture facilitates intelligent facial enhancement applications and the parallel processing effectively improves the efficiency of the algorithm. Qualitative and quantitative evaluations were performed for mask propagation and face enhancement. Comparisons with the previous methods and five representative commercial systems, including PicTreat, Portraiture, Portrait+, Meitu, and Baidu Motu, illustrate the robustness and effectiveness of our method.
A deformable elastic matching model for handwritten Chinese character recognition

This paper describes a deformable elastic matching approach to handwritten Chinese character recognition (HCCR). Handwritten character is regarded as a kind of deformable object, with elastic property. For the same category of character, we assume that different handwriting variations share the same topological structure, but may differ in shape details. The variations between different handwriting characters are modelled by a set of stroke displacement vectors (SDV). According to the SDV derived, a model character is deformed gradually, in an effort to transform itself much closer to an input character. Experiments show that the proposed elastic matching model can efficiently deal with local shape changes and variations between characters.
A Novel Vision based Finger-writing Character Recognition System

A new vision based finger writing character recognition system (FWCRS) is proposed in this paper. The FWCRS allows people to write characters virtually just using his finger-tip (we call this "finger-writing"). The trajectories of the finger-tip are tracked and reconstructed as a kind of inkless character pattern and finally recognized by a classifier. In this paper, a simple but effective background model is built for the FWCRS to segment human finger from cluttered background. A robust fingertip detection algorithm based on feature matching is presented. The finger-writing character is finally recognized by a DTW classifier. Experiments show that the FWCRS can recognize finger-writing uppercase & lowercase English characters with the accuracy of 95.6%, 98.5% respectively
Handwritten/Printed Receipt Classification Using Attention-Based Convolutional Neural Network

This paper presents an approach for the classification of handwritten and printed receipts based on a convolutional neural network (CNN). One of the main challenges related to such classification is the diversity of the background interference in the receipt images. To overcome this problem, we propose a new technique named "attention-based CNN" (ABCNN), inspired by the concept of "attention" in visual neuroscience. This approach helps us to focus on the receipt in an image without bounding box annotation. Our experimental results showed that the proposed ABCNN (i) significantly improves the classification accuracy compared to normal CNN (from 95% to 98.25%), and (ii) enables the network to process images directly without object detection, and (iii) it is faster to train and test the network.
Off-line Chinese handwriting recognition using multi-stage neural network architecture

In this paper, we propose a multi-stage neural network architecture (MNNA) which integrates several neural networks and various feature extraction approaches into a unique pattern recognition system. The general mechanism for designing the MNNA is presented. A three-stage fully connected feedforward neural networks system is designed for handwritten Chinese character recognition (HCCR). Different feature extraction methods are employed at each stage. Experiments show that the three-stage neural network based HCCR system achieved impressive performance and the preliminary results are very encouraging.
SwiftPost — A vision-based fast postal envelope identification system

A vision-based fast postal envelope identification system for moving machine printed Chinese postal envelopes is proposed. Our system uses a high-speed camera to capture the image of envelopes running on the convey device and then recognizes the postal address and postcode on the envelopes. A vocabulary of 4590 categories of characters are supported, which include 4516 frequently used Chinese characters defined in GB2312-80, 62 alphanumeric characters, and 12 punctuation marks and symbols. The supported font styles include Song, Fang Song, Kai, Hei, etc. with the printed font size of no less than 7.5 points. The experimental results on 761 mail images representing 25,060 characters show that an envelope with an average of 32.9 characters can be processed and recognized within 81.38 milliseconds and the character recognition rate of postal address is 98.72%. Furthermore, our system also provides the function to store the envelope images and their recognition results into database in real time, which can be used in subsequent envelopes tracking and management. The experimental results with live mails on site indicate that our system can reach a speed of 21,000 mails per hour, and the character recognition rate of postal address is as high as 98.92%. Besides, our system can be conveniently equipped on the envelope processing devices in postal service center.
Chinese handwriting quality evaluation based on analysis of recognition confidence

Handwriting is one of the major activities in everybody's life. In order to let people form good habit of writing, handwriting quality evaluation is an important and useful technology. In this paper, a method of Chinese handwriting quality evaluation is proposed based on analysis of online Chinese handwriting recognition confidence. For a given handwritten Chinese character, we firstly calculate the confidence level by the candidate distance obtained from the modified quadratic discriminant function (MQDF) classifier. Then the Chinese handwriting quality is evaluated according to different recognition confidence measurement levels. Experimental results show that the proposed method is reasonable and effective.
A local linear discriminant analysis method for handwritten Chinese character recognition

LDA is one of popular dimension reduction techniques in existing handwritten Chinese characters (HCC) recognition systems. To deal with the class separation problem and the multimodal samples in tradition LDA method, we proposed a new local linear discriminant analysis (LLDA) method for handwritten Chinese character recognition in this paper. The algorithm operates as follows: (1) Using the clustering algorithm to find clusters for each class. (2) Finding the nearest neighbor clusters for each cluster and using cluster means in the computation of the between-class scatter in LDA while keeping the within-class scatter unchanged. (3) Finally vector normalization is applied to further improve the class separation problem. A series of experiments on HCL2000 have indicated that our method can effectively improve the recognition, the error rate reduction reaches 14.8% comparing to the traditional LDA method, showing effectiveness of the proposed approach.
Handwritten Chinese Similar Characters Recognition Based On AdaBoost

As an effective statistical learning tool, AdaBoost has been widely used in the field of pattern recognition. Considering the large categories of Chinese characters and the recognition rate is low for similar character set, we propose a two-stage booting based scheme for Chinese character recognition. The AdaBoost procedure is applied to the similar character sets' recognition after the traditional distance classifier. Experimental results show that AdaBoost is much better than conventional distance classifier.
Joint Parameters Estimation for General Alpha Stable Random Noise

This paper is concerned with the parameters estimation of i.i.d. random noise subjects to general Alpha stable distribution (αS) (include symmetrical and skewed distribution). We discuss the joint parameters estimation scheme for the αS random series. We firstly estimate the parameter α and σ by means of the Empirical Character Function (ECF) method. Using the deskewed transform, we next propose a new method to estimate the location parameter μ. Then, using the estimations of μ and α and with the Fractional Lower Order Moment (FLOM) method, an improved estimator of skewness parameter β is proposed. Furthermore, with the estimations of β and μ in the case of α=1 employing ECF method, a hybrid joint estimation scheme of four parameters is presented. The simulation results show that the proposed method performs well on wide region of α in estimating β and μ in comparison with traditional ECF method.
PS-LSTM: Capturing Essential Sequential Online Information with Path Signature and LSTM for Writer Identification

Writer identification plays a significant role in several applications including forensic trace evidence identification, mobile bank transaction authentication, and handwritten character/text recognition. However, performing writer identification requires a considerable amount of experimental work and labor, as well as professional skills. In this paper, we propose a novel path-signature long short-term memory (PS-LSTM) recurrent neural network for writer identification that contributes as follows: 1) A mathematical feature set, path signature, is successfully applied to writer identification to characterize the essential geometric and analytic properties of pen-tip trajectory, which help distinguish between the diverse writing styles of different people, especially in confusing situations. 2) Different iterated integrals of path signature are investigated to model the local curvature nature of the pen-tip trajectory for writer identification. 3) Our study is the first to demonstrate the significance of integrating path signature with long short-term memory for writer identification. On Database CASIA-OLHWDN1.0, the proposed PS-LSTM writer identification achieves significantly superior results and outperforms previous works.
An open source testing tool for evaluating handwriting input methods

This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with the socket communication protocol. After the Android client receives the data, it simulates the handwriting on screen of client device, and triggers the corresponding handwriting recognition method. The recognition accuracy is recorded by the Android client. We present the design principles and describe the implementation of the test platform. We construct several test datasets for evaluating different handwriting recognition systems, and conduct an objective and comprehensive test using six Chinese handwriting input methods with five datasets. The test results for the recognition accuracy are then compared and analyzed.
Kernel Modified Quadratic Discriminant Function for Online Handwritten Chinese Characters Recognition

The modified quadratic discriminant function has been used successfully in handwriting recognition, which can be seen as a dot-product method by eigen- decomposition of the covariance matrix. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel- based method, Kernel modified quadratic discriminant function (KMQDF) for online Chinese Characters Recognition. Experimental results show that the performance of MQDF is improved by the kernel approach.
A Faster Method for Chinese Font Recognition Based on Harris Corner

A new fast font recognition method based on Harris corners is proposed in this paper. According to our observation, we find out that the font discriminative information is mainly hidden in some interesting points of characters. Based on this discovery, we extract the texture feature of the interesting points. The Harris corners are extracted as the interesting points. Experimental results show that our method is 20 times faster than the traditional Gabor features based method while keeping an almost equal accuracy.
A Method for Layout Evaluation of Online Handwritten Chinese Character Quality Based on Template

Evaluation of Chinese handwriting character quality is an important function of computer assisted Chinese learning technology it can point out the errors in a handwritten character and make objective assessment on the writing quality of the character. However, only a few studies were reported on this new research topic in the literature. In this paper, the main target of handwriting evaluation has been presented. The common layout errors in handwriting samples are summarized and then a new layout evaluation method is proposed. The method is consisted of three parts of stroke layout evaluation, component layout evaluation and entire character shape evaluation, through the application of nine assessment rules. The experiment results show that the method is capable for detecting layout errors of handwriting samples and making objective assessment on whether a character is written good or not.
Image coding using fuzzy vector quantizer

The fuzzy vector quantizer (FVQ) technique is applied to image coding when a square image subblock is mapped into a reproduction codeword. The FVQ is also tested on a database of Gauss Markov sources to demonstrate its performance in coding highly correlated data. An attempt is made to determine whether the use of softer decisions in the FVQ training step would optimize the codeword distribution and make the quantizer adapt well to random initial codebooks. The experiments show that FVQ coding performs better than standard methods.<<ETX>>
A PLSA-Based Semantic Bag Generator with Application to Natural Scene Classification under Multi-instance Multi-label Learning Framework

Classifying natural scenes into semantic categories has always been a challenging task. So far, many works in this field are primarily intended for single label classification, where each scene example is represented as a single instance vector. The multi-instance multi-label (MIML) learning framework proposed by Z.H. Zhou et al.  provides a new solution to the problem of scene classification in a different way. In this paper, we propose a novel scene classification method based on pLSA-based semantic bag generator and MIML learning framework. Under the framework of MIML learning, we introduce the mechanism that transfers an image into a set of instances through the pLSA-based bag generator. Experiments show that our approach achieves better classification performance comparing with the previous work.
Character-SIFT: A Novel Feature for Offline Handwritten Chinese Character Recognition

SIFT descriptor has been widely applied in computer vision and object recognition, but has not been explored in the field of handwritten Chinese character recognition. In this paper we proposed a novel SIFT based feature for offline handwritten Chinese character recognition. The presented feature is a modification of SIFT descriptor taking into account of the characteristics of handwritten Chinese samples. In our approach, global elastic meshing is first constructed and then the related gradient code of each sub-region is accumulated dynamically. Experiments using MQDF classifier show our feature’s effectiveness with a recognition rate of 97.868%, which outperforms original SIFT feature and two traditional features, Gabor feature and gradient feature.
Segmentation of On-line Cursive Handwritten Chinese Word Based on Stroke Speed Feature and Stroke Vector Feature

On-line handwritten Chinese word recognition has recently become an important research topic in the filed of computer vision. However, the segmentation of cursive Chinese word is still an unsolved problem. In this paper, two new features, stroke speed feature and stroke vector feature, are proposed for the segmentation of on-line handwritten Chinese word. Analysis and experiments show that both of the features are easy to implement, with low computation complexity and encouraging correct segmentation accuracy. Furthermore, the stroke vector feature outperforms traditional histogram method and we found it is especially suitable for the segmentation of cursive handwritten word where two characters touch each other or overlap.
Facial Skin Beautification Using Region-Aware Mask

In this paper, we present a facial attractiveness enhancement system for three major skin attributes: homogeneity, lighting, and color. To implement specific facial manipulation, we propose a new region-aware mask generation approach based on the state-of-the-art edit propagation techniques. The region-aware masks allow the user to beautify faces without the tedious and time-consuming region selection. In our system, an input portrait is decomposed into smoothness, lighting and color layers by edge-preserving filter. Then, taking the facial landmarks and significant edges as the constraint, layer masks are generated based on the image-guided energy minimization framework. The system could beautify faces automatically using the attractiveness priors of example-sets and psychological knowledge. Experiments illustrate the effectiveness of our method on various face databases, and the comparison with the previous methods indicates the reliability of our region-aware mask.
The generic object categorization using the Latent Dirichlet allocation model and bag of Biologically Inspired Model features

The generic image categorization was a challenging problem because of the wide variety of objects in image. In this paper, we proposed a method using the hybrid generative/discriminative approach combining the Biologically Inspired Model (BIM) to implement the generic object categorization. The main contributions were below: 1) We proposed an feature extraction method, which adjust BIM, and formed bag of BIM(BOBIM) feature. 2) We used LDA model to extract the semantic topics, and used SVM to make final decision. The LDA/SVM model was a hybrid generative/discriminative approach. The experimental results reveal the efficiency of our method.