Handwritten digit and letter recognition is one of the oldest and a very important topic in the field of pattern recognition. Handwritten digit and letter recognition poses different problem because of different writing styles, similarity in structure and angle of orientation. Therefore it is very important to find effective method for recognition and classification of digit and letter. Handwritten digit and letter recognition has various applications such as number plate recognition, extracting business card information, bank check processing, postal address processing, passport processing, signature processing etc. This paper propose a method of handwritten digit and letter recognition using feature extraction based on hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). These extracted features are passed to K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers for classification. Standard MNIST and EMNIST letter dataset are used for this experiment. Firstly MNIST digit and EMNSIT letter dataset are binarized and later stray pixels are removed. Features are extracted using hybrid Discrete Wavelet Transform and Discrete Cosine Transform. KNN and SVM classifiers are used for classification purpose. The proposed method was able to obtain a highest accuracy of 97.74% for digit and 89.51% for letter using SVM classifier.

Handwritten Digit and Letter Recognition Using Hybrid DWT-DCT with KNN and SVM Classifier

Ability for accurate digit recognizer modelling and prediction is critical for pattern recognition and security. A variety of classification machine learning algorithms are known to be effective for digit recognition. The purpose of this experiment is rapid assessment of multiple types of classification models on digit recognition problem. The work offers an environment for comparing four types of classification models in a unified experiment: Multiclass decision forest, Multiclass decision jungle, Multiclass Neural Network and Multiclass Logistic Regression. The work presents assessment results using 6 performance metrics: Overall accuracy, Average accuracy, Micro-averaged precision, Macro-averaged precision, Micro-averaged recall and Macro-averaged recall. The experimental results showed that the highest accuracy was obtained by Multiclass Neural Network with the value of 97.14%. The operational tool has been published to the web and openly available at Azure Machine Learning Studio for experiments and extensions.

A Comparative Study on Handwritten Digit Recognizer using Machine Learning Technique

Identification of handwritten digits, letters, mathematical symbols and complex structure expressions have captured a lot of concentration in the field of pattern recognition. Accuracy has been improved by considering the features like skew, entropy, kurtosis, standard deviation. In segmentation binarization, edge detection, morphological operation has been considered. The equations under various categories have been considered for experiment and achieved significant results. Latency, throughput and accuracy have been improved by using feed forward back propagation neural network with gradient descent with momentum algorithm and adaptive learning.

A new neural network based algorithm for identifying handwritten mathematical equations

In many real-life classification problems, we may not get exact class labels for training samples. One such example is bandit feedback in multiclass classification. In this setting, we only get to know whether our predicted label is correct or not. Due to which, we are left in uncertainty about the actual class label when we predict the wrong class. This paper proposes exact passive-aggressive online algorithms for multiclass classification under bandit feedback (EPABF). The proposed approach uses an exploration-exploitation strategy to guess the class label in every trial. To update the weights, we solve a quadratic optimization problem under multiple class separability constraints and find the exact solution. We do this by finding active constraints using the KKT conditions of the optimization problem. These constraints form a support set that determines the classes for which the weight vector needs to be updated. We propose three different variants of the weight update rule, which vary based on the aggressiveness to correct the mistake. These are called EPABF, EPABF-I, and EPABF-II. We also provide mistake bounds for the proposed EPABF, EPABF-I, and EPABF-II. Experiments demonstrated that our proposed algorithms perform better than other bandit feedback-based approaches and comparably to the full information approaches.

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Exact Passive-Aggressive Algorithms for Multiclass Classification Using Bandit Feedbacks

The task for handwritten digit recognition has been troublesome due to various variations in writing styles. Therefore, we have tried to create a base for future researches in the area so that the researchers can overcome the existing problems. The existing methods and techniques for handwritten digit recognition were reviewed and understood to analyze the most suitable and best method for digit recognition. A number of 60,000 images were used as training sets of images with pixel size of 28×28. The images/training sets were matched with original image. It was found out after complete analysis and review that classifier ensemble system has the least error rate of just 0.32%. In this paper, review of different methods handwritten digit recognition were observed and analyzed.

Handwritten Digit Recognition Using Machine Learning: A Review

Handwritten digit recognition is an important research topic in computer vision and pattern recognition. This paper proposes an effective handwritten digit recognition approach based on specific multi-feature extraction and deep analysis. First, we normalize images of various sizes and stroke thickness in preprocessing to eliminate negative information and keep relevant features. Secondly, considering that handwritten digit image recognition is different from traditional image semantics recognition, we propose specific feature definitions, including structure features, distribution features and projection features. Moreover, we fuse multiple features into the deep neural networks for semantics recognition. Experiments results on benchmark database of MNIST handwritten digit images show that the performance of our algorithm is remarkable and demonstrate its superiority over several existing algorithms.

Caiyun Ma

Papers

Effective handwritten digit recognition based on multi-feature extraction and deep analysis