Finding Logo and Seal in Historical Document Images - An Object Detection Based Approach

Chanda, Sukalpa; Prasad, Prashant Kumar; Hast, Anders; Brun, Anders; Mårtensson, Lasse; Pal, Umapada

doi:10.1007/978-3-030-41404-7_58

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Noting that the region-based techniques rely heavily on the accuracy of the segmentation step, which can be difficult in less structured documents with complex layout. In [8] logo and seal in historical documents. Here, there is no need for any preprocessing step like binarization or layout-analysis.…”

Section: Related Workmentioning

confidence: 99%

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Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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Section: Related Workmentioning

confidence: 99%

“…An evaluation of this criterion for many texture features allows us to argue our choice of the relevant feature characterizing the different textures in a passport page. The characterization degree F S is given by the equation (8):…”

Section: Figurementioning

confidence: 99%

Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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Fast and Accurate Deep Learning Model for Stamps Detection for Embedded Devices

Gayer

Ershova²,

Arlazarov³

2022

Pattern Recognit. Image Anal.

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Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection

Saxton,

Dong,

Bode

et al. 2024

Geosciences

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This study presents a novel AI method for extracting polygon and point features from historical geologic maps, representing a pivotal step for assessing the mineral resources needed for energy transition. Our innovative method involves using map units in the legends as prompts for one-shot segmentation and detection in geological feature extraction. The model, integrated with a human-in-the-loop system, enables geologists to refine results efficiently, combining the power of AI with expert oversight. Tested on geologic maps annotated by USGS and DARPA for the AI4CMA DARPA Challenge, our approach achieved a median F1 score of 0.91 for polygon feature segmentation and 0.73 for point feature detection when such features had abundant annotated data, outperforming current benchmarks. By efficiently and accurately digitizing historical geologic map, our method promises to provide crucial insights for responsible policymaking and effective resource management in the global energy transition.

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Finding Logo and Seal in Historical Document Images - An Object Detection Based Approach

Cited by 3 publications

References 22 publications

Neural Network-based System for Automatic Passport Stamp Classification

Neural Network-based System for Automatic Passport Stamp Classification

Fast and Accurate Deep Learning Model for Stamps Detection for Embedded Devices

Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection

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