Handwritten Symbol Recognition Research Articles

CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems

The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model.

Low-Cost Online Handwritten Symbol Recognition System in Virtual Reality Environment of Head-Mounted Display

Virtual reality is an important technology in the digital media industry, providing a whole new experience for most people. However, its manipulation method is more difficult than the traditional keyboard and mouse. In this research, we proposed a new low-cost online handwriting symbol recognition system to accurately identify symbols by user actions. The purpose was low cost processing without requiring a server. Experimental results showed that the average success rate of recognition was 99.8%. The execution time averaged a significantly low 0.03395 s. The proposed system is, respectively, highly reliable and at a low cost. This implies that the proposed system is suitable for applications in real-time environments.

Back Propagation Neural Network (BPNN) Based Recognition Of Handwritten Mathematical Equations

The recognition of handwritten mathematical symbols and equations are the critical and challenging issue in the field of pattern recognition. It is the need to recognize complicated handwritten mathematical equations viz. law of gravity, convolution integral etc. The issues like overwriting of symbols, characters etc. are identified and solved it by selecting best classifier to improve recognition rate. The machine learning approach with enhanced multi layer precentor feed forward back propagation neural network algorithm with an offline mode of recognition has been used to improve throughput, accuracy and overall efficiency of mathematical equations recognition. The hybrid features extracted viz. centroid, boundary box, zoning density, line segment etc. and gradient descent with momentum training algorithm has been used. Adaptive learning is used to carry out the experiment on numerous kinds of equations. Through experimental result, the system is evaluated and illustrated which shows the significant improvement 93.5 % accuracy in recognition of simple as well as complicated mathematical equations. In future current methodology will be the key factor to initiate paperless work and digital world.

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

Online recognition of handwritten music symbols

In this paper, we propose an effective online method to recognize handwritten music symbols. Based on the fact that most music symbols can be regarded as combinations of several basic strokes, the proposed method first classifies all the strokes comprising an input symbol and then recognizes the symbol based on the results of stroke classification. For stroke classification, we propose to use three types of features, which are the size information, the histogram of directional movement angles, and the histogram of undirected movement angles. When combining classified strokes into a music symbol, we utilize their sizes and spatial relation together with their combination. The proposed method is evaluated using two datasets including HOMUS, one of the largest music symbol datasets. As a result, it achieves a significant improvements of about 10% in recognition rates compared to the state-of-the-art method for the datasets. This shows the superiority of the proposed method in online handwritten music symbol recognition.

Recognition approach for ballot symbol based on run features

Recognition of hand-written ballot symbols is crucial in vote-counting system based on image understanding.To improve the accuracy of ballot symbol recognition,a symbol recognition approach based on run feature was proposed.Firstly,the concept of run was presented and a discriminant model was established based on run features of ballot symbols.Secondly,the relative positions of runs were described with a ternary tree.In addition,the main run regions were extracted in noisy environment by merging run regions,and an approach of recognizing ambiguous symbols was also given.Finally,the experimental results show that the run features can accurately reflect the geometrical features of ballot symbols.The proposed approach achieves high accuracy,and its accuracy is 6.07% higher than that of template matching algorithm.

On-line recognition of handwritten symbols

An online system for recognizing handwritten symbols from a user specified alphabet is described. The symbols are written on a digitising tablet. When a symbol is subsequently written the system is required to recognize the symbol irrespective of the scale, orientation and position of the written symbol.

Towards an art based mathematical editor, that uses on-line handwritten symbol recognition

A new mathematical editor, based on the recognition of run-on discrete handwritten symbols, is proposed. The tested laboratory prototype of the system, modular and adaptable to the user habits and site requirements, uses a natural handwriting interface as well as human gestures. Two methods were used for symbol recognition, namely the state-of-the-art elastic matching algorithm and an Adaptive Resonance Theory neural architecture. The neural solution is proved to be better adapted to the cognitive nature of the problem and faster in both learning and test phases. Finally a novel attribute grammar permits the detection and subsequent correction of errors in the mathematical expressions.

Recognition of handwritten symbols

New statistical methods are presented for recognizing isolated, handwritten or printed symbols directly from raster images of documents such as technical drawings and maps. Our methods for recognizing symbols of known or unknown rotation avoid the traditional thinning and vectorization steps in the recognition process. Hence, the information loss caused by these two steps is eliminated. In all of our approaches we consider the outer pixel boundary of an isolated symbol candidate in the binary raster image as a simple closed curve. This curve is then approximated by a parametric spline curve, an elliptic Fourier expansion of the Fourier expansion due to Zahn and Roskies. Curvature values and coordinates along the spline curves or the coefficients of the Fourier expansion are then used as descriptors in a statistical classification scheme. Statistical classification using these methods was done on separate training and test sets consisting of the digits 0 to 9 and all lower-case letters. There were about one hundred samples of each handwritten symbol class in each set. The correct classification rate was as high as 97.7% using the parametric spline approximation, and 98.6% using the elliptic Fourier descriptors.

Recognition of hand-written symbols by orientation-selective filtering: [formula omitted]. Siemens Corporate Research and Support, Inc., 105 College Road East, Princeton, NJ 08540 U.S.A.

Recognition of hand-written symbols by orientation-selective filtering: [formula omitted]. Siemens Corporate Research and Support, Inc., 105 College Road East, Princeton, NJ 08540 U.S.A.

VLSI architecture for dynamic time-warp recognition of handwritten symbols

The method of dynamic time warping is a well-established technique for time alignment and comparison of speech and image patterns. It has found extensive application in speech recognition and related areas of pattern matching. Comparing the handwritten symbol to the set of training symbols (called reference symbols), we can recognize the input handwritten symbol by computing the distances among the input symbol and the reference symbols in the training set. In this paper we propose a VLSI architecture based on the space-time domain expansion which can compute the symbol distance and also give the index pairs which correspond to the warp function. The time complexity is O(max(m, n)) by using m × n processing elements array, where m is the length of the input symbol and n is the length of the reference symbol. With a uniprocessor, the matching process will have the time complexity O(m × n). If there are p reference symbols, using the proposed architecture, the recognition problem can be solved in time O(max(m, n, p)). With a uniprocessor, the time complexity will be O(m × n × p). The algorithm partition problems are discussed. Verification of the proposed VLSI architecture is also given.

Recognition of handwritten symbols in the process of writing

Recognition of handwritten symbols in the process of writing