Pincode detection using deep CNN for postal automation

Sharma, Nabin; Sengupta, Abira; Sharma, Rabi; Pal, Umapada; Blumenstein, Michael

doi:10.1109/ivcnz.2017.8402501

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Address or pin code may include numerals while word letters represent the city name, street name, or country name. Sharma et al [5] have presented a work for the postal automation. e CNN model to recognize and detect the pin codes is introduced.…”

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

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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%.

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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

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“…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. Few other authors have also used the holistic approach for the recognition of text, in which features are manually extracted [14,15], while others have used CNN for character recognition, in which features are automatically extracted [16].…”

Section: Related Workmentioning

confidence: 99%

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

Sharma

Gupta

et al. 2022

Scientific Programming

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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.

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“…Automatic recognition of the destination address of India is a very challenging task due to multi-lingual and multi-script behavior. For the postal system of developed countries, there exist many published research papers dealing with automation of postal documents written in non-Indian languages [1,2,11,12,13,14,24,25,27,28]. Most of the developed countries consider only a single script; hence, to develop postal system for such countries is easy.…”

Section: Introductionmentioning

confidence: 99%

A System for Recognition of Destination Address in Postal Documents of India

Roy

Pal²,

Roy³

et al. 2020

MJCS

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Recognition of destination address is compulsory for automation of the postal system in India. Our observation found that such recognition becomes a very challenging task due to inter-mixing of three languages (Hindi, English and the official language of the particular state in which the postal document is supposed to reach).In this paper, our attempt towards development of a dynamic programming based system for city-name and pin code recognition of destination address in postal documents of India not only managed to address the difficulties related to identification of the scripts but also managed to get rid of those problems which is generated due to character touching in postal documents. For city-name recognition, lexicon information is used. However, no lexicon information is used for pin code recognition since an Indian pin code contains only 6 digits. We obtained 99.55% reliability from tri-lingual city-name recognition system where error rates are 0.20% and rejection rates are 28.11%. From our experiment on recognition of handwritten pin codes, 99.01% reliability rate, 0.83% error rate and15.27% rejection rate are obtained. Furthermore, to enhance the city-name results by distributing the lexicon size district-wise, we conducted an experiment and presented the results.

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Pincode detection using deep CNN for postal automation

Cited by 8 publications

References 16 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

A System for Recognition of Destination Address in Postal Documents of India

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