Recognizing texts on maps

Current popular optical character recognition(OCR) models struggle to achieve accurate results in both text positioning and text labelling prediction due to the complex, diverse and noisy nature of historical maps. Oftentimes, text-bounding polygons in historical maps intersect each other, and te...

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Main Author: Tan, Pheng Khai
Other Authors: Li Boyang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/175181
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1751812024-04-19T15:42:31Z Recognizing texts on maps Tan, Pheng Khai Li Boyang School of Computer Science and Engineering boyang.li@ntu.edu.sg Computer and Information Science Current popular optical character recognition(OCR) models struggle to achieve accurate results in both text positioning and text labelling prediction due to the complex, diverse and noisy nature of historical maps. Oftentimes, text-bounding polygons in historical maps intersect each other, and texts are overlaid on top of surrounding geographical features, which add unnecessary noise and difficulties to the task of recognizing texts from maps. This paper presents a method to automatically generate large amounts of annotated training data by using CycleGAN to generate synthetic historical maps. The generated data is then used to train a DPText-DETR model, a model selected due to its distinct feature that gives it the potential to excel at the task required for this project. A pipeline is then proposed to be implemented to make historical map OCR more accessible and user-friendly. In this paper, thorough analysis and evaluation have been conducted on the proposed method, comparing it against a baseline model that adequately represents the pros and cons present in modern OCR tools, as well as a state-of-the-art model. Our evaluations show that this approach not only simplifies the generation of annotated training data but also significantly enhances OCR accuracy. Comparative performance assessments reveal that our model achieves a precision increase of 4.25 %, recall by 2.58%, and overall F1 improvement by 3.23% over baseline and state-of-the-art models in terms of Wolff’s metric, setting a new benchmark for historical map text recognition. Bachelor's degree 2024-04-19T12:11:31Z 2024-04-19T12:11:31Z 2024 Final Year Project (FYP) Tan, P. K. (2024). Recognizing texts on maps. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175181 https://hdl.handle.net/10356/175181 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Tan, Pheng Khai
Recognizing texts on maps
description Current popular optical character recognition(OCR) models struggle to achieve accurate results in both text positioning and text labelling prediction due to the complex, diverse and noisy nature of historical maps. Oftentimes, text-bounding polygons in historical maps intersect each other, and texts are overlaid on top of surrounding geographical features, which add unnecessary noise and difficulties to the task of recognizing texts from maps. This paper presents a method to automatically generate large amounts of annotated training data by using CycleGAN to generate synthetic historical maps. The generated data is then used to train a DPText-DETR model, a model selected due to its distinct feature that gives it the potential to excel at the task required for this project. A pipeline is then proposed to be implemented to make historical map OCR more accessible and user-friendly. In this paper, thorough analysis and evaluation have been conducted on the proposed method, comparing it against a baseline model that adequately represents the pros and cons present in modern OCR tools, as well as a state-of-the-art model. Our evaluations show that this approach not only simplifies the generation of annotated training data but also significantly enhances OCR accuracy. Comparative performance assessments reveal that our model achieves a precision increase of 4.25 %, recall by 2.58%, and overall F1 improvement by 3.23% over baseline and state-of-the-art models in terms of Wolff’s metric, setting a new benchmark for historical map text recognition.
author2 Li Boyang
author_facet Li Boyang
Tan, Pheng Khai
format Final Year Project
author Tan, Pheng Khai
author_sort Tan, Pheng Khai
title Recognizing texts on maps
title_short Recognizing texts on maps
title_full Recognizing texts on maps
title_fullStr Recognizing texts on maps
title_full_unstemmed Recognizing texts on maps
title_sort recognizing texts on maps
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175181
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