Convolution neural network based text image classifications
Convolution neural network(CNN) is a sensor with multiple layers, which is designed for identifying 2-dimensional images, with parallel processing ability, self-learning ability and good fault tolerance. In dealing with 2-dimensional graphics problems, especially for the identification of misplaceme...
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| מחברים אחרים: | |
| פורמט: | Final Year Project (FYP) |
| שפה: | English |
| יצא לאור: |
2017
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| נושאים: | |
| גישה מקוונת: | http://hdl.handle.net/10356/71667 |
| _version_ | 1826127712682508288 |
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| author | Zhou, Xiang |
| author2 | Yu Hao |
| author_facet | Yu Hao Zhou, Xiang |
| author_sort | Zhou, Xiang |
| collection | NTU |
| description | Convolution neural network(CNN) is a sensor with multiple layers, which is designed for identifying 2-dimensional images, with parallel processing ability, self-learning ability and good fault tolerance. In dealing with 2-dimensional graphics problems, especially for the identification of misplacement, zooming and other distortion invariant’s forms applications, it has a good robustness and operational efficiency, and it has been widely used in various types of image recognition This paper introduces its model principle and specific approaches, as well as its application in image classification, namely traffic sign identification and handwritten number recognition.
CNN combines the extracting features and identification process for training the neural network, and has achieved great success in the field of image classification. The experimental part of this paper uses CNN models for traffic sign and handwritten number recognition, and the correct rate is superior to other traditional methods. |
| first_indexed | 2024-10-01T07:13:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/71667 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:13:03Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/716672023-07-07T17:50:26Z Convolution neural network based text image classifications Zhou, Xiang Yu Hao School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Convolution neural network(CNN) is a sensor with multiple layers, which is designed for identifying 2-dimensional images, with parallel processing ability, self-learning ability and good fault tolerance. In dealing with 2-dimensional graphics problems, especially for the identification of misplacement, zooming and other distortion invariant’s forms applications, it has a good robustness and operational efficiency, and it has been widely used in various types of image recognition This paper introduces its model principle and specific approaches, as well as its application in image classification, namely traffic sign identification and handwritten number recognition. CNN combines the extracting features and identification process for training the neural network, and has achieved great success in the field of image classification. The experimental part of this paper uses CNN models for traffic sign and handwritten number recognition, and the correct rate is superior to other traditional methods. Bachelor of Engineering 2017-05-18T07:21:32Z 2017-05-18T07:21:32Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71667 en Nanyang Technological University 61 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Zhou, Xiang Convolution neural network based text image classifications |
| title | Convolution neural network based text image classifications |
| title_full | Convolution neural network based text image classifications |
| title_fullStr | Convolution neural network based text image classifications |
| title_full_unstemmed | Convolution neural network based text image classifications |
| title_short | Convolution neural network based text image classifications |
| title_sort | convolution neural network based text image classifications |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/71667 |
| work_keys_str_mv | AT zhouxiang convolutionneuralnetworkbasedtextimageclassifications |