Using the RRT algorithm to optimize classification systems for handwritten digits and letters

Radtke, Paulo V. W.; Sabourin, Robert; Wong, Tony

doi:10.1145/1363686.1364103

Cited by 8 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other works, letters from NIST SD 19 are also taken into account. For example, Radtke et al [89] optimized a classifier using an annealing-based approach called record-to-record travel reporting an accuracy of 93.78% for letters and 96.53% for digits. Koerich and Kalva [90] tested a multi-layer perceptron attaining an accuracy of 87.79% in the letters dataset.…”

Section: State Of the Artmentioning

confidence: 99%

A Survey of Handwritten Character Recognition with MNIST and EMNIST

2019

View full text Add to dashboard Cite

This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST's. In this paper, EMNIST is explained and some results are surveyed.

show abstract

Section: State Of the Artmentioning

confidence: 99%

A Survey of Handwritten Character Recognition with MNIST and EMNIST

2019

View full text Add to dashboard Cite

show abstract

“…The NIST dataset has been used occasionally in neural network systems. Many classifiers make use of only the digit classes [13], [14], whilst others tackle the letter classes as well [15]- [18]. Each paper tackles the task of formulating the classification tasks in a slightly different manner, varying such fundamental aspects as the number of classes to include, the training and testing splits, and the preprocessing of the images.…”

Section: Introductionmentioning

confidence: 99%

EMNIST: Extending MNIST to handwritten letters

Cohen

Afshar

Tapson

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

1,336

698

View full text Add to dashboard Cite

The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits.

show abstract

“…Other authors have used also letter from NIST Special Database 19. For example, Radtke et al [54] used recordto-record travel (accuracy of 96.53% for digits and 93.78% for letters), Koerich and Kalva [55] tested a multi-layer perceptron only with the letters dataset (accuracy of 87.79%. ), and Cavalin et al [56] used hidden Markov models (accuracy of 98% for digits and up to 90% for letters, though this result is only using uppercase letters, and the accuracy decreases to 87% when lowercase letters are also considered).…”

Section: The Emnist Database Emnist (Extended Mnist)mentioning

confidence: 99%

Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

2019

View full text Add to dashboard Cite

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches.

show abstract

Using the RRT algorithm to optimize classification systems for handwritten digits and letters

Cited by 8 publications

References 14 publications

A Survey of Handwritten Character Recognition with MNIST and EMNIST

A Survey of Handwritten Character Recognition with MNIST and EMNIST

EMNIST: Extending MNIST to handwritten letters

Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Contact Info

Product

Resources

About