City name recognition for Indian postal automation: Exploring script dependent and independent approach
Postal documents are often used for official communication, online shopping, etc. At times, the delivery gets delayed due to multiple scripts leading to the need for postal sorting facilities. Understanding the destination city name plays a major part in solving automatic sorting problems as the sam...
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| Veröffentlicht in: | Multimedia tools and applications 2024-03, Vol.83 (8), p.22371-22394 |
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| container_title | Multimedia tools and applications |
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| creator | Chatterjee, Somnath Mukherjee, Himadri Sen, Shibaprasad Obaidullah, Sk Md Roy, Kaushik |
| description | Postal documents are often used for official communication, online shopping, etc. At times, the delivery gets delayed due to multiple scripts leading to the need for postal sorting facilities. Understanding the destination city name plays a major part in solving automatic sorting problems as the same becomes more challenging due to the presence of handwritten documents. In order to develop an autonomous system to solve the problem, a Deep Learning-based system is proposed to recognize handwritten city names written in 6 major scripts namely Tamil, Roman, Devanagari, Bangla, Gurumukhi, and Arabic. Experiments were performed in both script-dependent (bi-stage) and independent approaches. In the bi-stage framework, we have obtained an average accuracy of
97.58
%
along with a back-end script recognition rate of
99.07
%
while in the script-independent approach, an accuracy of
97.03
%
was obtained on a dataset consisting of 807 classes. |
| doi_str_mv | 10.1007/s11042-023-16137-8 |
| format | Article |
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97.58
%
along with a back-end script recognition rate of
99.07
%
while in the script-independent approach, an accuracy of
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97.58
%
along with a back-end script recognition rate of
99.07
%
while in the script-independent approach, an accuracy of
97.03
%
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Understanding the destination city name plays a major part in solving automatic sorting problems as the same becomes more challenging due to the presence of handwritten documents. In order to develop an autonomous system to solve the problem, a Deep Learning-based system is proposed to recognize handwritten city names written in 6 major scripts namely Tamil, Roman, Devanagari, Bangla, Gurumukhi, and Arabic. Experiments were performed in both script-dependent (bi-stage) and independent approaches. In the bi-stage framework, we have obtained an average accuracy of
97.58
%
along with a back-end script recognition rate of
99.07
%
while in the script-independent approach, an accuracy of
97.03
%
was obtained on a dataset consisting of 807 classes.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-023-16137-8</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-3360-7576</orcidid></addata></record> |
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| subjects | Accuracy Automation Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Deep learning Documents Electronic commerce Engineering Handwriting recognition Multimedia Multimedia Information Systems Names Neural networks Postal sorting Scripts Special Purpose and Application-Based Systems |
| title | City name recognition for Indian postal automation: Exploring script dependent and independent approach |
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