Handwritten Bangla City Name Recognition Using Shape-Context Feature

Sahoo, Samanway; Nandi, Subham Kumar; Barua, Sourav; Pallavi, .; Malakar, Samir; Sarkar, Ram

doi:10.1007/978-981-10-7566-7_44

Cited by 6 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy obtained the rst model is 55.3%, and accuracy obtained by the other model is 75.1%. Sahoo et al [12] have introduced a method to recognize the Bangla handwritten city names. A Holistic approach is used.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Sharma

Gupta

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

Automation of Postal systems has the major research scope in the field of automation. To create Postal Automation set-up for countries like India is a tedious task if compared with other countries because of India’s multiscript and multilingual behavior. This work will help in recognizing the “Gurmukhi” handwritten district names of the State Punjab. To recognize the district names, a CNN-based architecture is proposed by employing a Holistic approach. For this, an image database of 22000 samples is prepared having 1000 sample images for every district name which is collected from 500 different writers. Maximum accuracy on validation data achieved by the proposed Model is 99%.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Sharma

Gupta

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…One such example is Vajda et al [9], where the authors have recognized pin codes written in Bangla as well as in English script using a nonsymmetric half-plane hidden Markov model and achieved an accuracy of 94.13% and 93%, respectively. Sahoo et al [10] have implemented a holistic approach for the recognition of city names written in Bangla script using shape-context features, while multiclassifiers are employed for the classification purpose. e datasets used here are the large datasets that are stored on the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Manchala et al [9] have used NN for the recognition of English script using the Holistic approach. Similarly, Bhowmik et al [10] have proposed a technique for the recognition of Bangla script. Wahbi et al [11] have worked on the recognition of Arabic script again by holistic approach, and the model employed for the recognition is hidden Markov model.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Sharma

Gupta

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

The challenges involved in the traditional cloud computing paradigms have prompted the development of architectures for the next generation cloud computing. The new cloud computing architectures can generate and handle huge amount of data, which was not possible to handle with the help of traditional architectures. Deep learning algorithms have the ability to process this huge amount of data and, thus, can now solve the problem of the next generation computing algorithms. Therefore, these days, deep learning has become the state-of-the-art approach for solving various tasks and most importantly in the field of recognition. In this work, recognition of city names is proposed. Recognition of handwritten city names is one of the potential research application areas in the field of postal automation For recognition using a segmentation-free approach (Holistic approach). This proposed work demystifies the role of convolutional neural network (CNN), which is one of the methods of deep learning technique. Proposed CNN model is trained, validated, and analyzed using Adam and stochastic gradient descent (SGD) optimizer with a batch size of 2, 4, and 8 and learning rate (LR) of 0.001, 0.01, and 0.1. The model is trained and validated on 10 different classes of the handwritten city names written in Gurmukhi script, where each class has 400 samples. Our analysis shows that the CNN model, using an Adam optimizer, batch size of 4, and a LR of 0.001, has achieved the best average validation accuracy of 99.13.

show abstract

“…It is a way of encoding the boundary of a contour by a string of numbers where each number represents a particular direction in which the next point on the contour is situated. In our work, we use the 8 way connected chain code [25] where the 8 directions are represented by the digits 0-7. Figure 11 describes the convention that is followed while extracting the chain code information.…”

Section: Chain Code Histogram (Cch)mentioning

confidence: 99%

Offline hand-drawn circuit component recognition using texture and shape-based features

Roy

Bhattacharya

Sarkar

et al. 2020

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

Circuit diagram is the very foundation of electrical and electronic sciences. A circuit diagram consists of various symbols called circuit components that specify the functionality of that circuit. Every day-today gadgets that we use are made up with a number of electrical/electronic circuits to play out their particular tasks. Till date circuit designers have to physically enter all data from the hand-drawn circuits into computers, and this procedure requires some investment in terms of time and carries mistakes with high likelihood. To this end, in this paper, we propose a method that relaxes this constraint by introducing a method for recognition of hand-drawn electrical and electronic circuit components, with both analog and digital components included. In the proposed method, the pre-processed images of circuit components are used for training and testing a recognition model using a feature set consisting of a texture based feature descriptor, called histogram of oriented gradients (HOG), and shape based features that include centroid distance, tangent angle, and chain code histogram. In addition, the texture based feature, being large in number compared to others is optimized using a feature selection algorithm called ReliefF. Classification of components is done by using sequential minimal optimization (SMO) classifier. The proposed method has been evaluated on a dataset of 20 different circuit components with 150 samples in each class. The experimental outcome shows that the proposed approach provides average 93.83% accuracy on the present database. We also compare our method with some of the state-of-the-art methods and we see that our method outperforms these methods.

show abstract

Handwritten Bangla City Name Recognition Using Shape-Context Feature

Cited by 6 publications

References 12 publications

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Offline hand-drawn circuit component recognition using texture and shape-based features

Contact Info

Product

Resources

About