Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

The problem of handwritten digit recognition has long been an open problem in the field of pattern classification and of great importance in industry. The heart of the problem lies within the ability to design an efficient algorithm that can recognize digits written and submitted by users via a tablet, scanner, and other digital devices. From an engineering point of view, it is desirable to achieve a good performance within limited resources. To this end, we have developed a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve the overall performance achieved in classification task, the literature suggests combining the decision of multiple classifiers rather than using the output of the best classifier in the ensemble; so, in this new approach, an ensemble of classifiers is used for the recognition of handwritten digit. The classifiers used in proposed system are based on singular value decomposition (SVD) algorithm. The experimental results and the literature show that the SVD algorithm is suitable for solving sparse matrices such as handwritten digit. The decisions obtained by SVD classifiers are combined by a novel proposed combination rule which we named reliable multi-phase particle swarm optimization. We call the method “Reliable” because we have introduced a novel reliability parameter which is applied to tackle the problem of PSO being trapped in local minima. In comparison with previous methods, one of the significant advantages of the proposed method is that it is not sensitive to the size of training set. Unlike other methods, the proposed method uses just 15 % of the dataset as a training set, while other methods usually use (60–75) % of the whole dataset as the training set. To evaluate the proposed method, we tested our algorithm on Farsi/Arabic handwritten digit dataset. What makes the recognition of the handwritten Farsi/Arabic digits more challenging is that some of the digits can be legally written in different shapes. Therefore, 6000 hard samples (600 samples per class) are chosen by K-nearest neighbor algorithm from the HODA dataset which is a standard Farsi/Arabic digit dataset. Experimental results have shown that the proposed method is fast, accurate, and robust against the local minima of PSO. Finally, the proposed method is compared with state of the art methods and some ensemble classifier based on MLP, RBF, and ANFIS with various combination rules.     

Adaptive classifiers for multisource OCR

When patterns occur in large groups generated by a single source (style consistent test data), the statistics of the test data differ from those of the training data, which consist of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize in the statistics of style-consistent test data. On NIST handwritten digit data, the adaptive classifiers reduce the error rate by more than 50% operating on one writer ( samples/class) at a time.Received: 14 November 2002, Accepted: 6 March 2003, Published online: 12 September 2003Correspondence to: George Nagy     

Automatic feature generation for handwritten digit recognition

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     

Recognition of Handwritten ZIP Codes in a Real—World Non-Standard-Letter Sorting System

In this article, we describe the OCR and image processing algorithms used to read destination addresses from non-standard letters (flats) by Siemens postal automation system currently in use by the Deutsche Post AG¹.We first describe the sorting machine, its OCR hardware and the sequence of image processing and pattern recognition algorithms needed to solve the difficult task of reading mail addresses, especially handwritten ones. The article concentrates mainly on the two classifiers used to recognize handprinted digits. One of them is a complex time delayed neural network (TDNN) used to classify scaled digit-features. The other classifier extracts the structure of each digit and matches it to a number of prototypes. Different digits represented by the same graph are then discriminated by classifiying some of the features of the digit-graph with small neural networks.We also describe some approaches for the segmentation of the digits in the ZIP code, so that the resulting parts can be processed and evaluated by the classifiers.     

纹理特征加权融合的中亚多文种文档图像文种识别

全球各地目前使用很多种相似的文种,相似文种的识别是模式识别领域内难点并迫切需要解决的问题之一。然而,针对中亚文种文本文档和少数民族文种也就是相似文种分类识别方面的文献报道几乎没有。首先建立了两个多文种文档图像数据库,分别有1 600幅和2 200幅纯文本整篇文档图像,包含英文,汉文,俄文,蒙文,阿拉伯文,藏文,维吾尔文,土耳其文,乌兹别克文,塔吉克文和哈萨克文等共有11种文档图像。其次分别提取文档图像的均值,标准差,熵,一致性,三阶矩,平滑度等六个纹理特征,利用不同7种分类器分类。在找到各个特征对多文种文本文档图像的灵敏度的基础上,采用加权特征融合方法提取融合特征,确定了适合中亚多文种文档图像识别的最佳权值。最后用不同分类器分类识别,通过多特征以系数加权融合之后,以建立的两个数据库基础下获得平均的识别率分别为99.38%和95.69%。实验结果表明,提取的纹理特征和加权融合的纹理特征能较好地描述文档图像特征,并且它们可以有效地分类以上所述的11个文种。     

基于多分类器组合的手写体数字识别

本文提出了一个基于多分类器组合的手写体数字识别方法。文中首先给出了一个客观评价分类器性能的参数,其后基于此参数提出了多分类器的组合方法,并从理论上研究了此方法的一些性质,本文实验采用Ｃｏｎｃｏｒｔｄｉａ大学模式识别与机器智能中心的手写体数字数据库,在实验中,使用了９个利用不同特征分类器进行组合,组合后识别率、拒识率和可靠性分别可达到９７．０５％,２．０５％,９９．０８％。     

Segmentation and recognition system for unknown-length handwritten digit strings

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.     

A novel free format Persian/Arabic handwritten zip code recognition system

In Iran like many other countries, the categorization of postal envelopes is executed manually, mostly based on the handwritten addresses and zip codes. That process is still slow and prone to man-made errors. Therefore, having an automated, accurate and efficient system to recognize handwritten zip codes is of high necessity for a faster and easier arrangement of postal envelopes, and consequently, enhanced performance of the post office.