Decision tree and deep learning based probabilistic model for character recognition

Sampath, A.; Gomathi, N.

doi:10.1007/s11771-017-3701-8

Cited by 25 publications

(10 citation statements)

References 16 publications

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“…Apart from the MediaLab LP benchmark data set and ISI_Bengali data set, we have assessed the performance of the proposed method using the Chars74K benchmark data set containing Kannada and English alphabets and achieved 98.64% which is almost equivalent to 98.56% reported by the method from [10] and better than 96% reported by [1]. The disadvantage of the methods [10] and [1] is that they are not scale and rotation independent. Table III shows the performance comparison of the method [18] from the literature with the proposed method using publicly available benchmark data sets.…”

Section: Resultsmentioning

confidence: 97%

“…K. Sampath et al in the paper [1] proposed a feature extraction technique for character recognition using combination of existing features such as histogram oriented Gabor features, grid level features (local gradient), and gray level co-occurrence matrix and reported a success rate of 96% using Chars74K data set. The concept of calculating moments has been central to some IP tasks [2] and applications like pathological brain detection problems, etc.…”

Section: Related Workmentioning

confidence: 99%

“…4. Find the distances from centroid C to all P k 's using the Equation (1). Preserve all the computed distances in a separate list called DISTANCES.…”

Section: A Scale and Rotation Independent Feature Extractionmentioning

confidence: 99%

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A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Soora¹,

Mohammed²,

Mohammed³

2020

IJACSA

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This paper presents a novel geometrical scale and rotation independent feature extraction (FE) technique for multilingual character recognition (CR). The performance of any CR techniques mainly depends on the robustness of the proposed FE methods. Currently, there are very few scale and rotation independent FE techniques present in the literature which successfully extract the robust features from characters with noise such as distortion and breaks in the characters. Many FE methods from the literature failed to distinguish the characters which look similar in their appearance. So, in this paper, we have proposed a novel scale and rotation independent geometrical shape FE technique which successfully recognized distorted, broken, and similarly looking characters. Aside from the proposed FE technique, we've used crossing count (CC) features. Finally, we have combined the proposed features with CC features to make as Feature Vector (FV) of the character to be recognized. The proposed CR technique is evaluated using publicly available media-lab license plate (LP), ISI_Bengali, and Chars74K benchmark data sets and achieved encouraging results. To further assess the performance of the proposed FE method, we've used a proprietary data set containing nearly 168000 multilingual characters from English, Devanagari, and Marathi scripts and achieved encouraging results. We have observed better classification rates for the proposed FE method using publicly available benchmark data sets as compared to few of the CR FE methods from the literature.

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Section: Resultsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Soora¹,

Mohammed²,

Mohammed³

2020

IJACSA

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“…Several studies [ 148 , [154] , [155] , [156] , [157] , [158] , [159] , [160] , [161] , [162] , [163] , [164] , [165] , [166] , [167] , [168] , [169] , [170] , [171] , [172] , [173] , [174] , [175] , [176] ] focused on the development and test of automatic systems to identify the writer of different available datasets. Some researches were based on deep learning systems and others used basic statistical tools to create a classification method.…”

Section: Handwriting/signaturementioning

confidence: 99%

Interpol review of questioned documents 2016–2019

Deviterne-Lapeyre

2020

Forensic Science International: Synergy

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“…Some novel configurations of deep neural networks like Convolutional Neural network (CNN), Long Short-term Memory (LSTM)/ Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), etc. have got a lot of appreciation and attention due to their superior learning characteristics and efficient classification performance [14][15][16][17][18]. These networks have also been implemented by various researchers for OCR problems with varying complexity.…”

Section: Introductionmentioning

confidence: 99%

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

Tripathi¹,

Patheja²

2021

IJACSA

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This paper presents a deep learning based intelligent text recognition system with touching and overlapped characters. The robustness and effectiveness in the proposed model are enhanced through the modified configuration of neural network known as Deep Wavelet Neural Network (DWNN). The capability of deep learning networks to learn efficiently from an unlabeled dataset has attracted the attention of many researchers over the last decade. However, the performance of these networks is subject to the quality of the dataset and invariant image representation. Numerous optical character recognition techniques have also been presented in the recent years, but the overlapped and touching characters have not been addressed much. The nonlinear and uncertain representation of image data in case of overlapped text adds severe complexity in the process of feature extraction and respective learning. The proposed architecture of DWNN uses fast decaying wavelet functions as activation function in place of conventional sigmoid function to cope up with the uncertainties and nonlinearity of the data representation in overlapped text images. It comprises of cascaded layered architecture of translated and dilated versions of wavelets as activation functions for the training and feature extraction at multiple levels. The local transformation and deformation variation in the visual data has also been taken care efficiently through the modified architecture of DWNN. Comprehensive experimental analysis has been performed over various test images to verify the effectiveness of the proposed text recognition system. The performance of the proposed method is assessed with the help of the metrics, namely, estimation error, cost function and accuracy. The proposed approach will be implemented in MATLAB.

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Decision tree and deep learning based probabilistic model for character recognition

Cited by 25 publications

References 16 publications

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Interpol review of questioned documents 2016–2019

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

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