One of the early works utilizing a relatively modern technique on this subject was [5]. In [6] however, the authors studied the performance of different classifier algorithms on the MNIST database of handwritten digits. They discussed measures that affect algorithmic implementation such as training time, run time, and memory requirements. In their work, they presented a Baseline Linear Classifier, Nearest Neighbor Classifier, Large Fully Connected Multi-Layer Neural Network, LeNet1, LeNet4, Boosted LeNet4 (based on the idea of Convolutional Networks), Tangent Distance Classifier (TDC), LeNet4 with K-NN, Local Learning with LeNet4, Optimal Margin Classifier (OMC). From these, the Boosted LeNet4 achieved the best error performance while LeNet4 required the least memory.
Work by Yawei Hou and Huailin Zhao utilized an Improved BP Neural Network for Handwritten Digit Recognition. The authors claim results obtained converged faster and the classification results were more accurate compared to results at that time [8].
In [9], the authors presented that Feedforward neural networks utilizing Extreme Learning machine algorithm had faster weight optimization however required
larger number
of hidden units to provide comparable results with a
Backpropaogation
based algorithm.
In [10], the authors presented a method for recognizing handwritten digits by fitting generative spline models which would then be tuned by an Expectation Maximization Algorithm. While the method has it
advntages
, the main advantage is higher computational requirements compared to standard OCR techniques.
Work by [11] involved the combination of classifiers for digit recognition. This work was based on the idea that either of Bayesian combination, Dempster-Shafer evidential reasoning and Dynamic classifier selection, independent decisions by two
high performance
nearest-neighbor hand-printed digit classifiers can be combined to obtain improved digit classification systems.
In 2003, Cheng-Lin Liu et al [12] summarized the performance of then state-of-the-art feature extraction and classifier techniques on three image databases: CEDAR, MNIST, CENPARMI. In total, 10 feature vectors and 8 classifiers were combined to give 80 accuracies to the test data sets used. Results obtained can be found in [12]. Similar work by the same author(s) evaluated normalization methods and direction feature extraction techniques with existing methods useful in digit recognition [13].
In
similar vein
, Loo-Nin Teow and Kia-Fock Loe in [14], presented a method based on
biological vision
to solve automatic recognition of handwritten digits. They extracted linearly separable features from MNIST dataset and used a linear discriminant system for recognition, with the
triowise
linear support vector machines with soft voting yielding the best results.
It doesn’t end there
however
. In 1999, [15] proposed contour information and Fourier descriptors for digit recognition. Models were built based on contour features, then test digits were analyzed by comparing the test digit’s features with built models. The recognition rate achieved
as
around 99.04%.
In [16], a three-stage classifier was developed comprising of 2 Neural Networks and one Support Vector Machine (SVM). The two Neural Networks in tandem help to provide low misclassification rate, more complex features, and, a well-balanced rejection criterion. The SVM was optimized to take the top classes ranked by the Neural network. The authors claimed their work to achieve competitive results at the time.
Numerous works, tricks, approaches, techniques, and systems can also be found on this subject. For instance:
The use of
Self organizing
maps [17]
Shape matching [18]
[19] A method involving the division of the image into grids and computing the Hu moments as features was proposed.
Artificial neural network
was then implemented as
classifier
. The method yielded good processing times and accuracy