共查询到20条相似文献,搜索用时 15 毫秒
1.
Sherif Abdleazeem Ezzat El-Sherif 《International Journal on Document Analysis and Recognition》2008,11(3):127-141
In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition. The performances
of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve
as a benchmark for future work on the problem. The performance of well known classifiers and feature extraction techniques
will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and
competes with the state-of-the-art techniques. A total of 54 different classifier/features combinations will be evaluated
on Arabic digits in terms of accuracy and classification time. The results are analyzed and the problem of the digit ‘0’ is
identified with a proposed method to solve it. Moreover, we propose a strategy to select and design an optimal two-stage system
out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy
as the highest classifier/features combination but with much less recognition time. 相似文献
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脱机手写数字识别方法 总被引:1,自引:2,他引:1
脱机手写体数字识别有着重大的使用价值,特征提取占据了重要的位置.提出了一种通过拓扑特征构造的特征提取新方法,利于了9种特征对数字进行特征提取,然后利用分类树的方法将数字进行分类.最后,在本科学生手写数字图像样本库上的试验结果表明,提出的特征提取方法不仅具有很快的运算能力,而且较大幅度地提高了识别率. 相似文献
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Song WANG Seiichi UCHIDA Marcus LIWICKI Yaokai FENG 《Frontiers of Computer Science》2013,7(4):514-525
In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various deformations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting. 相似文献
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Multimedia Tools and Applications - Supervised learning techniques require labeled examples that can be time consuming to obtain. In particular, deep learning approaches, where all the feature... 相似文献
7.
Shaukat Zeeshan Ali Saqib Farooq Qurat ul Ain Xiao Chuangbai Sahiba Sana Ditta Allah 《Multimedia Tools and Applications》2020,79(39-40):29537-29549
Multimedia Tools and Applications - Handwritten character recognition has been acknowledged and achieved more prominent attention in pattern recognition research community due to enormous... 相似文献
8.
Using generative models for handwritten digit recognition 总被引:2,自引:0,他引:2
Revow M. Williams C.K.I. Hinton G.E. 《IEEE transactions on pattern analysis and machine intelligence》1996,18(6):592-606
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Automatic feature generation for handwritten digit recognition 总被引:6,自引:0,他引:6
Gader P.D. Khabou M.A. 《IEEE transactions on pattern analysis and machine intelligence》1996,18(12):1256-1261
An automatic feature generation method for handwritten digit recognition is described. Two different evaluation measures, orthogonality and information, are used to guide the search for features. The features are used in a backpropagation trained neural network. Classification rates compare favorably with results published in a survey of high-performance handwritten digit recognition systems. This classifier is combined with several other high performance classifiers. Recognition rates of around 98% are obtained using two classifiers on a test set with 1000 digits per class 相似文献
12.
This paper presents an innovative approach called box method for feature extraction for the recognition of handwritten characters. In this method, the binary image of the character is partitioned into a fixed number of subimages called boxes. The features consist of vector distance (γ) from each box to a fixed point. To find γ the vector distances of all the pixels, lying in a particular box, from the fixed point are calculated and added up and normalized by the number of pixels within that box. Here, both neural networks and fuzzy logic techniques are used for recognition and recognition rates are found to be around 97 percent using neural networks and 98 percent using fuzzy logic. The methods are independent of font, size and with minor changes in preprocessing, it can be adopted for any language. 相似文献
13.
Artières T Marukatat S Gallinari P 《IEEE transactions on pattern analysis and machine intelligence》2007,29(2):205-217
We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and Asian characters, command gestures, symbols, small drawings, and geometric shapes. It can be used as a building block for a series of recognition tasks with many applications 相似文献
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This article focuses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by support vector machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples are generated by affine transformations and elastic distortions. Experiments are performed on the well-known MNIST database to validate the method and the results show that the system can outperform both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations. 相似文献
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基于组合分类器的自由手写体数字识别方法 总被引:1,自引:1,他引:0
自由手写体数字识别广泛应用于信息录入和文本识别中。基于组合分类器实现手写数字的识别,克服了单因子识别的局限性,识别中使用距离法和改进的BP神经网络方法,以多种特征向量作为分类器的输入,以举手法则确定识别输出。实验证明,该系统具有较高的识别率和极低的误识率,有令人鼓舞的应用价值。 相似文献
16.
The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art. 相似文献
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This paper presents an original hybrid MLP-SVM method for unconstrained handwritten digits recognition. Specialized Support Vector Machines (SVMs) are introduced to improve significantly the multilayer perceptron (MLP) performance in local areas around the separating surfaces between each pair of digit classes, in the input pattern space. This hybrid architecture is based on the idea that the correct digit class almost systematically belongs to the two maximum MLP outputs and that some pairs of digit classes constitute the majority of MLP substitutions (errors). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer achieves a recognition rate of
98.01%98.01\%
, for real mail zipcode digits recognition task. By introducing a rejection mechanism based on the distances provided by the local SVMs, the error/reject trade-off performance of our recognition system is better than several classifiers reported in recent research. 相似文献
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3D local shapes are a critical cue for object recognition in 3D point clouds. This paper presents an instance-based 3D object recognition method via informative and discriminative shape primitives. We propose a shape primitive model that measures geometrical informativity and discriminativity of 3D local shapes of an object. Discriminative shape primitives of the object are extracted automatically by model parameter optimization. We achieve object recognition from 2.5/3D scenes via shape primitive classification and recover the 3D poses of the identified objects simultaneously. The effectiveness and the robustness of the proposed method were verified on popular instance-based 3D object recognition datasets. The experimental results show that the proposed method outperforms some existing instance-based 3D object recognition pipelines in the presence of noise, varying resolutions, clutter and occlusion. 相似文献
19.
Huaigu Cao Venu Govindaraju Anurag Bhardwaj 《International Journal on Document Analysis and Recognition》2011,14(2):145-157
With the ever-increasing growth of the World Wide Web, there is an urgent need for an efficient information retrieval system
that can search and retrieve handwritten documents when presented with user queries. However, unconstrained handwriting recognition
remains a challenging task with inadequate performance thus proving to be a major hurdle in providing robust search experience
in handwritten documents. In this paper, we describe our recent research with focus on information retrieval from noisy text
derived from imperfect handwriting recognizers. First, we describe a novel term frequency estimation technique incorporating
the word segmentation information inside the retrieval framework to improve the overall system performance. Second, we outline
a taxonomy of different techniques used for addressing the noisy text retrieval task. The first method uses a novel bootstrapping
mechanism to refine the OCR’ed text and uses the cleaned text for retrieval. The second method uses the uncorrected or raw
OCR’ed text but modifies the standard vector space model for handling noisy text issues. The third method employs robust image
features to index the documents instead of using noisy OCR’ed text. We describe these techniques in detail and also discuss
their performance measures using standard IR evaluation metrics. 相似文献