A trainable feature extractor for handwritten digit recognition

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.     

Robust vision-based features and classification schemes for off-line handwritten digit recognition

We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images.     

脱机手写数字识别方法

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.     

KAFD Arabic font database

Font recognition is useful for improving optical text recognition systems’ accuracy and time, and to restore the documents’ original formats. This paper addresses a need for Arabic font recognition research by introducing an Arabic font recognition database consisting of 40 fonts, 10 sizes (ranging from 8 to 24 points) and 4 styles (viz. normal, bold, italic, and bold–italic). The database is split into three sets (viz. training, validation, and testing). The database is freely available to researchers.¹ Moreover, we introduce a baseline font recognition system for benchmarking purposes, and report identification rates on our KAFD database and the Arabic Printed Text Image (APTI) database with 20 and 10 fonts, respectively. The best recognition rates are achieved using log-Gabor filters.     

A modal learning adaptive function neural network applied to handwritten digit recognition

A novel combination of the adaptive function neural network (ADFUNN) and on-line snap-drift learning is presented in this paper and applied to optical and pen-based recognition of handwritten digits [E. Alpaydin, F. Alimoglu for Optical Recognition of Handwritten Digits and E. Alpaydin, C. Kaynak for Pen-Based Recognition of Handwritten Digits http://www.ics.uci.edu/~mlearn/databases/optdigits/http://www.ics.uci.edu/~mlearn/databases/pendigits/]. Snap-drift [S.W. Lee, D. Palmer-Brown, C.M. Roadknight, Performance-guided neural network for rapidly self-organising active network management (Invited Paper), Journal of Neurocomputing, 61C, 2004, pp. 5-20] employs the complementary concepts of common (intersection) feature learning (called snap) and LVQ (drift towards the input patterns) learning, and is a fast, unsupervised method suitable for on-line learning and non-stationary environments where new patterns are continually introduced. ADFUNN [M. Kang, D. Palmer-Brown, An adaptive function neural network (ADFUNN) for phrase recognition, in: The International Joint Conference on Neural Networks (IJCNN05), Montréal, Canada, 2005, D. Palmer-Brown, M. Kang, ADFUNN: An adaptive function neural network, in: The 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), Coimbra, Portugal, 2005] is based on a linear piecewise neuron activation function that is modified by a novel gradient descent supervised learning algorithm. It has recently been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability with no hidden neurons. The unsupervised single layer snap-drift is effective in extracting distinct features from the complex cursive-letter datasets, and the supervised single layer ADFUNN is capable of solving linearly inseparable problems rapidly. In combination within one network (SADFUNN), these two methods are more powerful and yet simpler than MLPs, at least on this problem domain. We experiment on SADFUNN with two handwritten digits datasets problems from the UCI Machine Learning repository. The problems are learned rapidly and higher generalisation results are achieved than with a MLP.     

手写数字识别的研究

为了提高手写数字识别的性能,研究了利用BP神经网络作为分类器在设计上的几个关键问题,给出每个关键环节的可行方案并进行有效总结.同时对脱机手写数字的图像预处理及特征提取部分的关键技术做了详细阐述.在此基础上给出分类器设计与训练的详细实验,实验结果表明,合理解决设计BP神经网络分类器的关键问题能够确保其对手写数字的高分类性能.     

Learning-based word spotting system for Arabic handwritten documents

The retrieval of information from scanned handwritten documents is becoming vital with the rapid increase of digitized documents, and word spotting systems have been developed to search for words within documents. These systems can be either template matching algorithms or learning based. This paper presents a coherent learning based Arabic handwritten word spotting system which can adapt to the nature of Arabic handwriting, which can have no clear boundaries between words. Consequently, the system recognizes Pieces of Arabic Words (PAWs), then re-constructs and spots words using language models. The proposed system produced promising result for Arabic handwritten word spotting when tested on the CENPARMI Arabic documents database.     

KHATT: An open Arabic offline handwritten text database

A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier.     

Local reference lines for handwritten phrase recognition

Reference line information has been used for diverse purposes in handwriting research, including word case classification, OCR, and holistic word recognition. In this paper, we argue that the commonly used global reference lines are inadequate for many handwritten phrase recognition applications. Individual words may be written at different orientations or vertically displaced with respect to one another. A function used to approximate the implicit baseline will not be differentiable or even continuous at some points. We have presented the case for local reference lines and illustrate its successful use in a system that verifies street name phrases in a postal application.     

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     

Cloud-based efficient scheme for handwritten digit recognition

Multimedia Tools and Applications - Handwritten character recognition has been acknowledged and achieved more prominent attention in pattern recognition research community due to enormous...     

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Handwritten digit recognition has long been a challenging problem in the field of optical character recognition and of great importance in industry. This paper develops a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve performance of isolated Farsi/Arabic handwritten digit recognition, we use Bag of Visual Words (BoVW) technique to construct images feature vectors. Each visual word is described by Scale Invariant Feature Transform (SIFT) method. For learning feature vectors, Quantum Neural Networks (QNN) classifier is used. Experimental results on a very popular Farsi/Arabic handwritten digit dataset (HODA dataset) show that proposed method can achieve the highest recognition rate compared to other state of the arts methods.     

Feature sub-set selection metrics for Arabic text classification

Feature sub-set selection (FSS) is an important step for effective text classification (TC) systems. This paper presents an empirical comparison of seventeen traditional FSS metrics for TC tasks. The TC is restricted to support vector machine (SVM) classifier and only for Arabic articles. Evaluation used a corpus that consists of 7842 documents independently classified into ten categories. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F₁ measures. Results reveal that Chi-square and Fallout FSS metrics work best for Arabic TC tasks.     

An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

Many variations of local binary pattern (LBP) were proposed to enhance its performance, including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS-LBP), center symmetric local ternary patterns (CS-LTP), center symmetric local multilevel pattern (CS-LMP), etc. In this paper, the accuracies of LBP technique and its variations are enhanced using four different sizes of a sliding window approach. This approach is used for investigating whether the features extracted by LBP are significant enough or its versions are needed as well. Five LBP-based techniques have been used including LBP, CS-LBP, CS-LTP, CS-LMP, and U2LBP. They have been applied to an Arabic digit image dataset called MAHDBase. Support vector machine (SVM) and random forests are utilized as classifiers. The experimental results show that the obtained accuracies have been improved by 19.56%, 21.43%, 5.63%, 6.51% and 5.62% for CS-LBP, CS-LMP, U2LBP, CS-LTP, and LBP, respectively, when the sliding window approach has been applied and SVM with linear kernel has been used as a classifier. Moreover, the results show that there is no need to use LBP variations to enhance the accuracy if the sliding window is applied because the highest accuracy has been acquired using LBP. At the end, the accuracy of proposed systems has been compared against other state-of-the-art LBP-based techniques showing the significance of the proposed systems.