Persian Handwritten Digits Research Articles

Persian handwritten digit, character and word recognition using deep learning

In spite of various applications of digit, letter and word recognition, only a few studies have dealt with Persian scripts. In this paper, deep neural networks are utilized through different DenseNet and Xception architectures, being further boosted by means of data augmentation and test time augmentation. Dividing the datasets to training, validation and test sets, and utilizing k-fold cross-validation, the comparison of the proposed method with various state-of-the-art alternatives is performed. Three datasets: HODA, Sadri and Iranshahr are used, which offer the most comprehensive collections of samples in terms of handwriting styles and the forms each letter may take depending on its position within a word. On the HODA dataset, we achieve recognition rates of 99.49% and 98.10% for digits and characters, being 99.72%, 89.99% and 98.82% for digits, characters and words from the Sadri dataset, respectively, as well as 98.99% for words from the Iranshahr dataset, each of which outperforms the performances achieved by the most advanced alternative networks, namely ResNet50 and VGG16. An additional contribution of the paper arises from its capability of words recognition as a holistic image classification. This improves the resulting speed and versatility significantly, as it does not require explicit character models, unlike earlier alternatives such as hidden Markov models and convolutional recursive neural networks. In addition, computation times have been compared with alternative state-of-the-art models and better performance has been observed.

Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

Mutual neighbors and diagonal loading-based sparse locally linear embedding

ABSTRACTIn this study, a new Locally Linear Embedding (LLE) algorithm is proposed. Common LLE includes three steps. First, neighbors of each data point are determined. Second, each data point is linearly modeled using its neighbors and a similarity graph matrix is constructed. Third, embedded data are extracted using the graph matrix. In this study, for each data point mutual neighborhood conception and loading its covariance matrix diagonally are used to calculate the linear modeling coefficients. Two data points will be named mutual neighbors, if each of them is in the neighborhood of the other. Diagonal loading of the neighboring covariance matrix is applied to avoid its singularity and also to diminish the effect of noise in the reconstruction coefficients. Simulation results demonstrate the performance of applying mutual neighborhood conception and diagonal loading and their combination. Also, the results of applying the mutual neighborhood on Laplacian Eigenmap (LEM) demonstrate the good performance of the proposed neighbor selection method. Our proposed method improves recognition rate on Persian handwritten digits and face image databases.

Fusion of LLE and stochastic LEM for Persian handwritten digits recognition

In this paper, a new local manifold learning (ML) method is proposed. Our proposed method, which is named FSLL, is based on the fusion of locally linear embedding (LLE) and a new Stochastic Laplacian Eigenmaps (SLEM). SLEM is the same as a common LEM technique, but the coefficients between each data point and its neighbors are calculated by a stochastic process. The coefficients of SLEM make a probability mass function scheme, and their entropy is set to a certain value. The entropy value is an estimation of the locality around each data point. Two criteria will be presented based on the mutual neighborhood conception to determine the entropy value. In LLE, each data point is linearly reconstructed based on its neighbors and then the embedded data manifold is extracted by preserving these linear reconstruction coefficients. LLE and SLEM extract and learn the embedded data manifold by two different kinds of local structure information. In FSLL, two local ML methods, SLEM and LLE, are fused by rewriting their cost functions without the need for any projection space. Fusion of these two techniques provides more structural information at high-dimensional space that can be applied on extracting the embedded low-dimensional data. Also, in this study, a feature vector will be presented by combining a HMAX feature vector and a PCA-based feature vector. Evaluations of the proposed method are done on Persian handwritten digit IFHCDB and IPHD databases in image and feature spaces. The results demonstrate the performance of FSLL and SLEM. The recognition rates are improved about 4% in most dimensionalities. Also, a method of out-of-sample test data extension is proposed corresponding to the proposed methods.

SHUFFLED FROG LEAPING ALGORITHM AND FEATURE SELECTION FOR IMPROVING RECOGNITION RATE OF PERSIAN HANDWRITTEN DIGITS CLASSIFIER

In this paper, Shuffled Frog Leaping Algorithm is used to improve the recognition rate of Persian handwritten digits. In proposed approach, the effective features in increasing the recognition rate are selected using the Binary Shuffled Frog Leaping Algorithm (BSFLA). By selecting the most suitable features from among all extracted features, the recognition rate is improved and computational costs are also decreased. The fitness function in BSFLA is the number of errors in the Fuzzy classifier which its minimum value is desired. The results indicate that Shuffled Frog Leaping algorithm (SFLA) is more efficient

A New Combined Feature Extraction Method for Persian Handwritten Digit Recognition

Feature extraction is one of the most important steps in Optical Character Recognition (OCR) systems, that is effective in recognition accuracy. In this paper, a suitable combination of different features such as zoning, hole size, crossing counts, etc. for Persian handwritten digits recognition is proposed. Due to high number of features, feature vector dimensions will be high that increases training time exponentially. In this paper, to solve this problem, Principal Component Analysis (PCA) method is employed for reducing the feature vector dimensions. Finally, data are classified by Support Vector Machine (SVM) classification method. The proposed method has been executed on HODA dataset which is one of the largest standard datasets of Persian handwritten digits that includes 60[Formula: see text]000 training and 20[Formula: see text]000 test samples. The proposed method reaches to 99.07% of accuracy in this dataset, and the experimental results show significant improvement in accuracy of Persian handwritten OCR compared to the previous methods.

Handwriting recognition of digits, signs, and numerical strings in Persian

This paper presents an important step towards the standardization of research works on Optical Character Recognition in Persian language. It describes the formations of a standard handwritten database, including isolated digits, isolated signs, multi-digit numbers, numerical strings, courtesy amounts, and postal codes. In this regard, binary images of 72,180 samples were extracted from the designed forms. These forms were filled by 180 writers selected from different ages, genders, and jobs. Then these forms were scanned at 300 dpi with a high-speed scanner. Finally, forms are segmented into samples and are stored in bitmap format. This database is named PHOND, Persian Handwritten Optical Numbers & Digits, and it is available to the research community. Comparisons with the previous related databases illustrate the advantages of PHOND against other databases. Different experiments are done using PHOND database and the results are compared with other research works in handwritten recognition.

Persian Handwritten Digit Recognition Using Ensemble Classifiers

Abstract Optical character recognition (OCR) includes three main sections, pre-processing, feature extraction and classification. The purpose of the pre-processing is to remove noise, smooth and normalize the input data, which can have a significant role in better differentiating patterns in the feature space. In the feature extraction, a feature vector is assigned to each sample which represents the sample in the related feature space and thus makes it distinct from the other samples. Feature extraction has significant effect on classification of sample class. In the classification stage, correct boundaries should be made between feature vectors, so that the samples of each pattern are separated from other samples by clear boundaries. Persian handwritten digits recognition is a branches of pattern recognition. In this paper, a method is proposed to recognize Persian handwritten digits. The proposed framework includes three main sections, pre-processing, feature extraction and classification. In the feature extraction stage, an appropriate and complementary set of features consist of 115 features extracted from Persian handwritten digits. In the classification stage, the ensemble classifier algorithm is used to separate the samples’ classes from each other. Estimation of results was performed on TMU (Tarbiat Modares University) digits database and the best recognition rate of Persian handwritten digits, was 95.280%.

Off-line Recognition of Persian Handwritten Digits using Statistical Concepts

In this paper a novel method for recognition of Persian handwritten digits is proposed. This approach uses central moments, Covariance, Median and Variance that are obtained at each the box digit image as the features set. The features set consist of 140 dimensions for each instance. In the classification phase of our proposed method the support vector machines, K-Nearest Neighbor and Sequential Minimal Optimization separately are employed. In this paper is used the principal components analysis (PCA) to reduce dimension features set. the performance of these three classifiers is observed on this application in terms of the correct classification and misclassification and how the performance of K-Nearest Neighbor classifier can be improved by varying the value of k.To evaluate our proposed scheme a database of Persian handwritten digits consist of 1699 handwritten digit images is used. In the best case proposed scheme using SMO classifiers yields a recognition rate of 92.3875% for handwritten Farsi numerals.

Persian handwritten digits recognition: A divide and conquer approach based on mixture of MLP experts

Persian handwritten digits recognition: A divide and conquer approach based on mixture of MLP experts

FPGA Simulation of Linear and Nonlinear Support Vector Machine

Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers on FPGA is presented. Training phase of the SVM is performed offline, and the extracted parameters used to implement testing phase of the SVM on the hardware. In the architecture, vector multiplication operation and classification of pairwise classifiers is designed in parallel and simultaneously. In order to realization, a dataset of Persian handwritten digits in three different classes is used for training and testing of SVM. Graphically simulator, System Generator, has been used to simulate the desired hardware design. Implementation of linear and nonlinear SVM classifier using simple blocks and functions, no limitation in the number of samples, generalized to multiple simultaneous pairwise classifiers, no complexity in hardware design, and simplicity of blocks and functions used in the design are view of the obvious characteristics of this research. According to simulation results, maximum frequency of 202.840 MHz in linear classification, and classification accuracy of 98.67% in nonlinear one has been achieved, which shows outstanding performance of the hardware designed architecture.

Recognition of Persian handwritten digits using Characterization Loci and Mixture of Experts

A method for recognition of Persian handwritten digits based on characterization loci and mixture of experts is proposed. This method utilizes the characterization loci, as the main feature. In the classification stage of our proposed method the mixture of experts are applied. This recognition method is applied to Farsi hand-written digits in the HODA database. The experimental results support our claim that this method improves the performance compare with conventional methods. Evaluating the proposed method with 20000 test samples and ME with 4 experts that each expert has 25 neurons in hidden layer and 5 neurons in hidden layers for gating network the recognition rate of 97.52% is achieved. Comparison with some of the most related methods indicates that the proposed method yields good recognition rate in Persian handwritten digits recognition. .