Machine learning based characters recognition
This final year project aims to study and implement some machine learning techniques for character recognition. The author was tasked to develop a mobile app for a business card scanner based on these techniques. The author has chosen to do research on Tesseract, which is an open-source optical char...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/75485 |
| _version_ | 1811693653174779904 |
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| author | Song, Tianyi |
| author2 | Huang Guangbin |
| author_facet | Huang Guangbin Song, Tianyi |
| author_sort | Song, Tianyi |
| collection | NTU |
| description | This final year project aims to study and implement some machine learning techniques for character recognition. The author was tasked to develop a mobile app for a business card scanner based on these techniques. The author has chosen to do research on Tesseract, which is an open-source optical character recognition (OCR) engine sponsored by Google and has embedded the Tess-two library locally into the business card scanner. The scanner was developed for Android systems. It is able to scan characters on business cards, distinguish the information and save it into the entry attributes for a new contact. It includes functions of photo cropping and saving, character recognition, information extraction and contact adding. The app design, app structure, key codes and testing results will be included in this report. Since OCR is the key technology of the application, its principle and development will be discussed for basic understanding as well as future improvement of the scanner. |
| first_indexed | 2024-10-01T06:55:06Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/75485 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:55:06Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/754852023-07-07T16:19:53Z Machine learning based characters recognition Song, Tianyi Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This final year project aims to study and implement some machine learning techniques for character recognition. The author was tasked to develop a mobile app for a business card scanner based on these techniques. The author has chosen to do research on Tesseract, which is an open-source optical character recognition (OCR) engine sponsored by Google and has embedded the Tess-two library locally into the business card scanner. The scanner was developed for Android systems. It is able to scan characters on business cards, distinguish the information and save it into the entry attributes for a new contact. It includes functions of photo cropping and saving, character recognition, information extraction and contact adding. The app design, app structure, key codes and testing results will be included in this report. Since OCR is the key technology of the application, its principle and development will be discussed for basic understanding as well as future improvement of the scanner. Bachelor of Engineering 2018-05-31T08:34:57Z 2018-05-31T08:34:57Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75485 en Nanyang Technological University 42 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Song, Tianyi Machine learning based characters recognition |
| title | Machine learning based characters recognition |
| title_full | Machine learning based characters recognition |
| title_fullStr | Machine learning based characters recognition |
| title_full_unstemmed | Machine learning based characters recognition |
| title_short | Machine learning based characters recognition |
| title_sort | machine learning based characters recognition |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/75485 |
| work_keys_str_mv | AT songtianyi machinelearningbasedcharactersrecognition |