Classification and recognition of handwritten digits by using mathematical morphology

Kumar, V. Vijaya; Srikrishna, A.; Babu, B. Raveendra; Mani, M. Radhika

doi:10.1007/s12046-010-0031-z

Cited by 21 publications

(3 citation statements)

References 8 publications

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“…Other mathematical morphology techniques used with high success on image processing purposes were applied for recognition of handwritten digits [19], denoising using morphological filters, license plate detection, text extraction. Also, a high rate of success was developed in biomedical image analysis [20].…”

Section: Figure 1 Flowchart Of the Proposed Algorithmmentioning

confidence: 99%

COVID-19 approved mask detection using mathematical morphology

Abrudan

Drăgulinescu²,

Preda³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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Face detection has multiple applications including recognition, people identification and detection of facial expressions. With the current pandemic crisis and due to the measures imposed to prevent Covid-19 spreading, the wearing of a protection mask became mandatory. The object of interest of this paper is to detect the wearing of an approved mask face using Viola Jones algorithm, aggregate channel features (ACF) and mathematical morphology. COVID-19 virus spread through the air, so it is necessary that all the materials used for manufacturing of the face masks to filter properly the air, and only the approved face masks to be used in order to control the spread infection. The algorithm used for face detection is Viola Jones with notable success in real time face detection and real time impression speed in face detection. Identification of the approved face mask against Covid-19 virus is made with a trained ACF detector. Eye detection, necessary to check if the face mask is properly placed, is based on mathematical morphology operators. These operations used together are robust with high results on the image processing.

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Section: Figure 1 Flowchart Of the Proposed Algorithmmentioning

confidence: 99%

COVID-19 approved mask detection using mathematical morphology

Abrudan

Drăgulinescu²,

Preda³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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“…Monu Agrawal et al, [4] have proposed a strategy to reduce the time and memory requirements in handwritten recognition by applying prototyping as an intermediate step in the synthetic pattern generation technique. Vijayakumar et al, [9] have proposed a novel algorithm for recognition of handwritten digits by classifying digits into two groups. One consists of blobs with/without stems and the other digits with stems only.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Neural Network in Classifying Handwritten Arabic Numerals

Kathirvalavakumar¹,

Palaniappan²

2016

JILSA

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A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.

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“…Kumar et al [10] have proposed unconstrained offline handwritten numeral recognition system using local and global features of profile of numeral image, majority voting scheme and neural network. Vijayakumar et al [19] have proposed a novel algorithm for recognizing handwritten digits. They have grouped the digits into blobs with stem and without stem.…”

Section: Introductionmentioning

confidence: 99%

Efficient Handwritten Numeral Recognition System Using Leaders of Separated Digit and RBF Network

Kathirvalavakumar¹,

Selvi²,

Palaniappan³

2014

Lecture Notes in Computer Science

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Abstract. In this paper an efficient method has been proposed to classify handwritten numerals using leader algorithm and Radial Basis Function network. Handwritten numerals are represented in matrix form and clusters with leaders are formed for each row of each digit separately. Every leader is with single target digit. Duplication patterns are avoided from the cluster leaders by combining those in a single pattern with target vectors having corresponding bits in on mode. Now resultant target vectors are with 10 bits corresponding to the number of digits considered for classification. Constructed leaders are trained using Radial Basis Function network. Experimental results show that the minimum number of patterns are enough for training compared to total patterns and it has been observed that convergency is fast during training. Also the number of resultant leaders after avoiding duplication patterns are less and the number of bits in each resultant pattern is 12.

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Classification and recognition of handwritten digits by using mathematical morphology

Cited by 21 publications

References 8 publications

COVID-19 approved mask detection using mathematical morphology

COVID-19 approved mask detection using mathematical morphology

Ensemble Neural Network in Classifying Handwritten Arabic Numerals

Efficient Handwritten Numeral Recognition System Using Leaders of Separated Digit and RBF Network

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