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Improved optical character recognition with deep learning

Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. For instance, OCR is typically used in many computer vision applications such as in automatic signboard recognition, language translati...

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Main Author: Tan, Chiang Wei
Format: Thesis
Language:English
Published: 2018
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/79061/1/TanChiangWeiMFKE2018.pdf
http://eprints.utm.my/id/eprint/79061/
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spelling my.utm.790612018-09-27T05:21:12Z http://eprints.utm.my/id/eprint/79061/ Improved optical character recognition with deep learning Tan, Chiang Wei TK Electrical engineering. Electronics Nuclear engineering Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. For instance, OCR is typically used in many computer vision applications such as in automatic signboard recognition, language translation as well as in the process of digitizing scanned documents. However, compared to old documents or poorly printed documents, printed characters are typically broken and blurred, which makes the character recognition in potentially far more complicated. Although there are several OCR applications which utilizes techniques such as feature extraction and template matching for recognition, these methods are still not accurate enough for recognition. In this work, deep learning network (transfer learning with Inception V3 model) is used to train and perform OCR. Deep learning network is implemented and trained using Tensorflow Python API that supports Python 3.5+ (GPU version) which is available under the Apache 2.0 open source license. The Inception V3 network is trained with 53,342 character images consisting of noises which are collected from receipts and newspapers. From the experiment results, the system achieved significantly better recognition accuracy on poor quality of text character level and resulted in an overall 21.5% reduction in error rate as compared to existing OCRs. Besides, there is another experiment conducted to further analyze the root causes of text recognition failure and a solution to overcome the problem is also proposed. Analysis and discussion were also made on how the different layer’s properties of neural network affects the OCR’s performance and training time. The proposed deep learning based OCR has shown better accuracy than conventional methods of OCR and has the potential to overcome recognition issue on poor quality of text character. 2018-01 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79061/1/TanChiangWeiMFKE2018.pdf Tan, Chiang Wei (2018) Improved optical character recognition with deep learning. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:108423
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Tan, Chiang Wei
Improved optical character recognition with deep learning
description Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. For instance, OCR is typically used in many computer vision applications such as in automatic signboard recognition, language translation as well as in the process of digitizing scanned documents. However, compared to old documents or poorly printed documents, printed characters are typically broken and blurred, which makes the character recognition in potentially far more complicated. Although there are several OCR applications which utilizes techniques such as feature extraction and template matching for recognition, these methods are still not accurate enough for recognition. In this work, deep learning network (transfer learning with Inception V3 model) is used to train and perform OCR. Deep learning network is implemented and trained using Tensorflow Python API that supports Python 3.5+ (GPU version) which is available under the Apache 2.0 open source license. The Inception V3 network is trained with 53,342 character images consisting of noises which are collected from receipts and newspapers. From the experiment results, the system achieved significantly better recognition accuracy on poor quality of text character level and resulted in an overall 21.5% reduction in error rate as compared to existing OCRs. Besides, there is another experiment conducted to further analyze the root causes of text recognition failure and a solution to overcome the problem is also proposed. Analysis and discussion were also made on how the different layer’s properties of neural network affects the OCR’s performance and training time. The proposed deep learning based OCR has shown better accuracy than conventional methods of OCR and has the potential to overcome recognition issue on poor quality of text character.
format Thesis
author Tan, Chiang Wei
author_facet Tan, Chiang Wei
author_sort Tan, Chiang Wei
title Improved optical character recognition with deep learning
title_short Improved optical character recognition with deep learning
title_full Improved optical character recognition with deep learning
title_fullStr Improved optical character recognition with deep learning
title_full_unstemmed Improved optical character recognition with deep learning
title_sort improved optical character recognition with deep learning
publishDate 2018
url http://eprints.utm.my/id/eprint/79061/1/TanChiangWeiMFKE2018.pdf
http://eprints.utm.my/id/eprint/79061/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:108423
_version_ 1643658086774210560
score 13.211869

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