Convolutional neural networks applied to handwritten mathematical symbols classification

Ramadhan, Irwansyah; Purnama, Bedy; Faraby, Said Al

doi:10.1109/icoict.2016.7571941

Cited by 21 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature [14] takes Simple Linear Iterative Clustering (SLIC) to segment mathematical formulas, and the segmented symbols are classified by the pretrained SqueezeNet model. Ramadhan et al [15] designed a CNN model trained on the images transformed from the CHROME 2014 training set, but this method did not significantly improve the recognition rate. Dong and Liu [16] carefully designed a new network CNN framework called VGG-HMS.…”

Section: Related Workmentioning

confidence: 99%

“…Dong and Liu [16] carefully designed a new network CNN framework called VGG-HMS. Unlike the literature [14], [15], the framework is characterized by increasing the depth of the network and making full use of the resources of the network, which achieves the highest recognition rate in the CROHME2014 dataset, and the recognition rate in the CROHME2016 dataset is second only to Myscript [20].…”

Section: Related Workmentioning

confidence: 99%

“…As revealed in Table 2, the model proposed in this paper achieves the highest accuracy for IHFSR. From the perspective of model and feature selection, CNN [15] is superior to traditional machine learning methods (SVM-RBF [18], AdaBoost [11], GLVQ [13]), which is higher by 2% to 3%. However, it is lower than the method of combining online, such as MLP + RNN [13], [20], CNN + LSTM [19], RNN [17], [20].…”

Section: Comparison With Existing Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Fang

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols. The system proposed in this paper integrates the eightdirectional feature of the original sequence in the convolutional layer, which significantly compensates for the lost dynamic trajectory information in the handwritten formula symbol. Meanwhile, the joint loss is constructed to improve the discriminability of features in the way of supervised learning, which enlarges the inter-class difference and decreases inner-class similarity. The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed SE-MCNN approach outperforms the state-of-the-art methods even at the condition of without using the data augmentation. INDEX TERMS Artificial neural network, multi-feature, joint training, discriminative feature.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With Existing Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Fang

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…e ability to automatically extract important features of an object leads to high performance in CNN. Applications of CNNs include in-scene text recognition to obtain image encrypted information [33] and improving handwritten mathematical symbols classification [34]. Moreover, Steganalysis on JPEG images was introduced in [35].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Despite extensive research, recognition of Persian and Arabic manuscripts is still a challenging problem due to the complicated and irregular nature of writing, wide vocabulary, and diversity of handwritings. In Persian and Arabic words, letters are joined together, and signs such as dots are placed above or below letters. In the proposed approach, the words are first decomposed into their constituent subwords to enhance the recognition accuracy. Then the signs of subwords are extracted to develop a dictionary of main subwords and signs. The dictionary is then employed to train a classifier. Since the proposed recognition approach is based on unsigned subwords, the classifier may make a mistake in recognizing some subwords of a word. To overcome this, a new subword fusion algorithm is proposed based on the similarity of the main subwords and signs. Here, convolutional neural networks (CNNs) are utilized to train the classifier. An autoencoder (AE) network is employed to extract appropriate features. Thus, a hybrid network is developed and named AECNN. The known Iranshahr dataset, including nearly 17000 images of handwritten names of 503 cities of Iran, was employed to analyze and test the proposed approach. The resultant recognition accuracy is 91.09%. Therefore, the proposed approach is much more capable than the other methods known in the literature.

show abstract

“…proposed [11] and recent researches have demonstrated its effectiveness on improving neural network for handwritten symbol image classification [6,9,14,19]. Convolutional neural network extends the ordinary artificial neural network in three important properties: local receptive field, shared weights, and sub-sampling [11].…”

Section: Networkmentioning

confidence: 99%

Context-based Multi-stage Offline Handwritten Mathematical Symbol Recognition using Deep Learning

Guan

View full text Add to dashboard Cite

We propose a multi-stage machine learning (ML) architecture to improve the accuracy of offline handwritten mathematical symbol recognition. In the first stage, we train and assemble multiple deep convolutional neural networks to classify isolated mathematical symbols. However, certain ambiguous symbols are hard to classify without the context information of the mathematical expressions where the symbols belong. In the second stage, we train a deep convolutional neural network that further classifies the ambiguous symbols based on the context information of the symbols. To further improve the classification accuracy, in the third stage, we develop a set of rules to classify the ambiguity or otherwise the syntax of the mathematical expressions will be violated. We evaluate the proposed method by using the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) dataset. The proposed method results the state-of-the-art accuracy of 94.04%, which is 1.62% improvement compared with the previous single-stage approach.

show abstract

Convolutional neural networks applied to handwritten mathematical symbols classification

Cited by 21 publications

References 12 publications

Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)

Context-based Multi-stage Offline Handwritten Mathematical Symbol Recognition using Deep Learning

Contact Info

Product

Resources

About