Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection

• URL: http://arxiv.org/abs/2112.06104v1
• Date: Sun, 12 Dec 2021 00:27:03 GMT
• Title: Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection
• Authors: Zekun Li, Runyu Guan, Qianmu Yu, Yao-Yi Chiang and Craig A. Knoblock
• Abstract summary: We propose a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We show that the state-of-the-art text detection models can benefit from the synthetic historical maps.
• Score: 5.872532529455414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-ofdomain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open- StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations easily. However, the cartographic styles between OSM map tiles and historical maps are significantly different. This paper proposes a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We use a style transfer model to convert contemporary map images into historical style and place text labels upon them. We show that the state-of-the-art text detection models (e.g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.

Related papers

• Detecting Omissions in Geographic Maps through Computer Vision [18.36056648425432]
  We develop and evaluate a method for automatically identifying maps that depict specific regions and feature landmarks with designated names. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks.
  arXiv  Detail & Related papers  (2024-07-15T13:26:58Z)
• Efficiently Leveraging Linguistic Priors for Scene Text Spotting [63.22351047545888]
  This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models. We generate text distributions that align well with scene text datasets, removing the need for in-domain fine-tuning. Experimental results show that our method not only improves recognition accuracy but also enables more accurate localization of words.
  arXiv  Detail & Related papers  (2024-02-27T01:57:09Z)
• CLIM: Contrastive Language-Image Mosaic for Region Representation [58.05870131126816]
  Contrastive Language-Image Mosaic (CLIM) is a novel approach for aligning region and text representations. CLIM consistently improves different open-vocabulary object detection methods. It can effectively enhance the region representation of vision-language models.
  arXiv  Detail & Related papers  (2023-12-18T17:39:47Z)
• CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence [9.652629004863364]
  We develop a large-scale benchmark dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. These well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval.
  arXiv  Detail & Related papers  (2023-12-14T01:54:38Z)
• The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps [7.209761597734092]
  mapKurator is an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection.
  arXiv  Detail & Related papers  (2023-06-29T16:05:40Z)
• SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding [57.108301842535894]
  We introduce SNAP, a deep network that learns rich neural 2D maps from ground-level and overhead images. We train our model to align neural maps estimated from different inputs, supervised only with camera poses over tens of millions of StreetView images. SNAP can resolve the location of challenging image queries beyond the reach of traditional methods.
  arXiv  Detail & Related papers  (2023-06-08T17:54:47Z)
• SpaText: Spatio-Textual Representation for Controllable Image Generation [61.89548017729586]
  SpaText is a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map. We show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-conditional-based.
  arXiv  Detail & Related papers  (2022-11-25T18:59:10Z)
• An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images [6.962949867017594]
  This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images. We have implemented the approach in a system called mapKurator.
  arXiv  Detail & Related papers  (2021-12-03T01:44:38Z)
• MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale [1.5894241142512051]
  We present MapReader, a free, open-source software library written in Python for analyzing large map collections (scanned or born-digital) MapReader allows users with little or no computer vision expertise to retrieve maps via web-servers. We show how the outputs from the MapReader pipeline can be linked to other, external datasets.
  arXiv  Detail & Related papers  (2021-11-30T17:37:01Z)
• Semantic Image Alignment for Vehicle Localization [111.59616433224662]
  We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera. In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
  arXiv  Detail & Related papers  (2021-10-08T14:40:15Z)
• Cross-Descriptor Visual Localization and Mapping [81.16435356103133]
  Visual localization and mapping is the key technology underlying the majority of Mixed Reality and robotics systems. We present three novel scenarios for localization and mapping which require the continuous update of feature representations. Our data-driven approach is agnostic to the feature descriptor type, has low computational requirements, and scales linearly with the number of description algorithms.
  arXiv  Detail & Related papers  (2020-12-02T18:19:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.