A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

Application of VQ-HMM to Chinese Spoken Digit Recognition

In this paper, a new speech recognition method was proposed, which integrated a VQ distortion measure and a discrete HMM. The VQ HMM uses a VQ distortion measure at each state instead of a discrete output probability used by a discrete HMM. The VQ HMM is described, and its speech recognition performance is compared with the conventional HMMs through the experiments on speaker independent Chinese spoken digit recognition. The comparisons confirm that the new method over performed traditional HMMs.

An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.

Research on Handwritten Chinese Character Recognition Based on BP Neural Network

The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before.Handwritten Chinese character recognition,as a hot research object in image pattern recognition,has many applications in people’s daily life,and more and more scholars are beginning to study off-line handwritten Chinese character recognition.This paper mainly studies the recognition of handwritten Chinese characters by BP(Back Propagation)neural network.Establish a handwritten Chinese character recognition model based on BP neural network,and then verify the accuracy and feasibility of the neural network through GUI(Graphical User Interface)model established by Matlab.This paper mainly includes the following aspects:Firstly,the preprocessing process of handwritten Chinese character recognition in this paper is analyzed.Among them,image preprocessing mainly includes six processes:graying,binarization,smoothing and denoising,character segmentation,histogram equalization and normalization.Secondly,through the comparative selection of feature extraction methods for handwritten Chinese characters,and through the comparative analysis of the results of three different feature extraction methods,the most suitable feature extraction method for this paper is found.Finally,it is the application of BP neural network in handwritten Chinese character recognition.The establishment,training process and parameter selection of BP neural network are described in detail.The simulation software platform chosen in this paper is Matlab,and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition.Design the GUI interface of human-computer interaction based on Matlab,show the process and results of handwritten Chinese character recognition,and analyze the experimental results.

Information Moment for Chinese Character Recognition

Moment invariants firstly introduced by M. K Hu in 1962, has some shortcomings. After counting a large number of statistical distribution information of Chinese characters,the authors put forward the concept of information moments and demonstrate its invariance to translation,rotation and scaling.Also they perform the experiment in which information moments compared with moment invaiants for the effects of similar Chinese characters and font recognition.At last they show the recognition rate of 88% by information moments,with 70% by moment inariants.

Processing Chinese hand-radicals activates the medial frontal gyrus A functional MRI investigation

Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.

A Dialectal Chinese Speech Recognition Framework

A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-related knowledge are adopted to transform a standard Chinese （or Putonghua, abbreviated as PTH） speech recognizer into a dialectal Chinese speech recognizer. Two kinds of knowledge sources are explored： one is expert knowledge and the other is a small dialectal Chinese corpus. These knowledge sources provide information at four levels： phonetic level, lexicon level, language level, and acoustic decoder level. This paper takes Wu dialectal Chinese （WDC） as an example target language. The goal is to establish a WDC speech recognizer from an existing PTH speech recognizer based on the Initial-Final structure of the Chinese language and a study of how dialectal Chinese speakers speak Putonghua. The authors propose to use contextindependent PTH-IF mappings （where IF means either a Chinese Initial or a Chinese Final）, context-independent WDC-IF mappings, and syllable-dependent WDC-IF mappings （obtained from either experts or data）, and combine them with the supervised maximum likelihood linear regression （MLLR） acoustic model adaptation method. To reduce the size of the multipronunciation lexicon introduced by the IF mappings, which might also enlarge the lexicon confusion and hence lead to the performance degradation, a Multi-Pronunciation Expansion （MPE） method based on the accumulated uni-gram probability （AUP） is proposed. In addition, some commonly used WDC words are selected and added to the lexicon. Compared with the original PTH speech recognizer, the resulting WDC speech recognizer achieves 10-18% absolute Character Error Rate （CER） reduction when recognizing WDC, with only a 0.62% CER increase when recognizing PTH. The proposed framework and methods are expected to work not only for Wu dialectal Chinese but also for other dialectal Chinese languages and even other languages.

An Introduction to the Chinese Speech Recognition Front-End of the NICT/ATR Multi-Lingual Speech Translation System

This paper introduces several important features of the Chinese large vocabulary continuous speech recognition system in the NICT/ATR multi-lingual speech-to-speech translation system. The features include： （1） a flexible way to derive an information rich phoneme set based on mutual information between a text corpus and its phoneme set; （2） a hidden Markov network acoustic model and a successive state splitting algorithm to generate its model topology based on a minimum description length criterion; and （3） advanced language modeling using multi-class composite N-grams. These features allow a recognition performance of 90% character accuracy in tourism related dialogue with a real time response speed.

Improving the Syllable-Synchronous Network SearchAlgorithm for Word Decoding in ContinuousChinese Speech Recognition

The previously proposed syllable-synchronous network search (SSNS) algorithm plays a very important role in the word decoding of the continuous Chinese speech recognition and achieves satisfying performance. Several related key factors that may affect the overall word decoding effect are carefully studied in this paper, including the perfecting of the vocabulary, the big-discount Turing re-estimating of the N-Gram probabilities, and the managing of the searching path buffers. Based on these discussions, corresponding approaches to improving the SSNS algorithm are proposed. Compared with the previous version of SSNS algorithm, the new version decreases the Chinese character error rate (CCER) in the word decoding by 42.1% across a database consisting of a large number of testing sentences (syllable strings).

Parallel compact integration in handwritten Chinese character recognition

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.

Chinese Speech Recognition Model Based on Activation of the State Feedback Neural Network

This paper proposes a simplified novel speech recognition model, the state feedback neural network activation model (SFNNAM), which is developed based on the characteristics of Chinese speech structure. The model assumes that the current state of speech is only a correction of the last previous state. According to the “C V”(Consonant Vowel) structure of the Chinese language, a speech segmentation method is also implemented in the SFNNAM model. This model has a definite physical meaning grounded on the structure of the Chinese language and is easily implemented in very large scale integrated circuit (VLSI). In the speech recognition experiment, less calculations were need than in the hidden Markov models (HMM) based algorithm. The recognition rate for Chinese numbers was 93.5% for the first candidate and 99.5% for the first two candidates.

A New Linguistic Decoding Method for Online Handwritten Chinese Character Recognition

This paper presents a new linguistic decoding method for online handwritten Chinese character recognition. The method employs a hybrid language model which combines N-gram and linguistic rules by rule quantification technique. The linguistic decoding algorithm consists of three stages: word lattice construction, the optimal sentence hypothesis search and self-adaptive learning mechanism. The technique has been applied to palmtop computer's online handwritten Chinese character recognition. Samples containing millions of characters were used to test the linguistic decoder. In the open experiment, accuracy rate up to 92% is achieved, and the error rate is reduced by 68%.

Current Status of Objectification of Four Diagnostic Methods on Constitution Recognition of Chinese Medicine

Chinese medicine(CM)has thousands of years of experience in prevention of diseases.As for CM,people's constitution is closely related to their health status,thus recognition of CM constitution is the fundamental and core contentof research on constitution types.With development of technologies such as sensors,arificial intelligence and big data,objectification of the four diagnostic methods of CM has gradually matured,bringing changes in the mindset and innovations in technical means for recognition of CM constitution.This paper presents a systematic review of the latest research trends in constitution recognition based on objectification of diagnostic methods in CM.

A Bit Progress on Word-Based Language Model

A good language model is essential to a postprocessing algorithm for recognition systems. In the past, researchers have presented various language models, such as character based language models, word based language model, syntactical rules language model, hybrid models, etc . The word N gram model is by far an effective and efficient model, but one has to address the problem of data sparseness in establishing the model. Katz and Kneser et al. respectively presented effective remedies to solve this challenging problem. In this study, we proposed an improvement to their methods by incorporating Chinese language specific information or Chinese word class information into the system.

A study on continuous Chinese speech recognition based on stochastic trajectory models

After pointed the unreasonableness of the three basic assumptions contained in HMM, we introduce the theory and the advantage of Stochastic najectory Models (STMs) that possibly resolve these problems caused by HMM assumptions. In STM, the acoustic observations of an acoustic unit are represented as clusters of trajectories in a parameter space.The trajectories are modelled by mixture of probability density functions of random sequence of states. After analyzing the characteristics of Chinese speech, the acoustic units for continuous Chinese speech recognition based on STM are discussed and phone-like units are suggested. The performance of continuous Chinese speech recognition based on STM is studied on VINICS system. The experimental results prove the efficiency of STM and the consistency of phone-like units.

2-D EAG Method for the Recognition of Hand-Printed Chinese Characters

A method, called Two-Dimensional Extended Attribute Grammars (2-DEAGs). for the recognition of hand-printed Chinese characters is presented. This method uses directly two dimensional information, and pro- vides a scheme for dealing with various kinds of specific cases in a uniform way. In this method, components are drawn in guided and redundant way and reductions are made level by level just in accordance with the com- ponent combination relations of Chinese characters. The method provides also polysemous grammars, coexisting grammars and structure inferrings which constrain redundant recognition by comparison among similar characters or components and greatly increase the tolerance ability to distortion.

Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration

It is of great significance to guarantee the efficient statistics of high-speed railway on-board equipment fault information,which also improves the efficiency of fault analysis. Considering this background, this paper presents an empirical exploration of named entity recognition(NER) of on-board equipment fault information. Based on the historical fault records of on-board equipment, a fault information recognition model based on multi-neural network collaboration is proposed. First, considering Chinese recorded data characteristics, a method of constructing semantic features and additional features based on character granularity is proposed. Then, the two feature representations are concatenated and passed into the gated convolutional layer to extract the dependencies from multiple different subspaces and adjacent characters in parallel. Next, the local features are transmitted to the bidirectional long short-term memory(BiLSTM) to learn long-term dependency information. On top of BiLSTM, the sequential conditional random field(CRF) is used to jointly decode the optimized tag sequence of the whole sentence. The model is tested and compared with other representative baseline models. The results show that the proposed model not only considers the language characteristics of on-board fault records, but also has obvious advantages on the performance of fault information recognition.