Image-based historical manuscript dating using contour and stroke fragments

He, Sheng; Samara, Petros; Burgers, J.W.J.; Schomaker, Lambert

doi:10.1016/j.patcog.2016.03.032

Cited by 41 publications

(31 citation statements)

References 54 publications

(114 reference statements)

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“…They achieved an accuracy of 85.1% using KNN classifier. Sheng He et al [6] explored the fragments of stroke and local contour. These strokes and local contour features are extracted and studied to determine for dating the historical documents.…”

Section: Related Workmentioning

confidence: 99%

“…If PSF is centrally symmetric, then the phase of h(p, q) has only two values. It can be expressed as shown in equation (6). Pages 95 164 199 267 311 323 386 259 372 241 241 Classes 1300 1325 1350 1375 1400 1425 1450 1475 1500 1525 1550 Text blocks 281 281 146 192 234 272 304 199 298 298 Thus, the phase information is invariance between G(x, y) and F(x, y) can express as in equation 7.…”

Section: Feature Extractionmentioning

confidence: 99%

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Classification of Historical Documents Based on LBP and LPQ Techniques

Gonasagi,

Rumma,

Hangrge*

2020

IJITEE

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Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Classification of Historical Documents Based on LBP and LPQ Techniques

Gonasagi,

Rumma,

Hangrge*

2020

IJITEE

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“…Jae et al [4] identify style-sensitive groups of patches for cars and street view images in order to model stylistic differences across time and space. He et al [3] and Li et al [5] address the task of estimating the age of historical documents. While He et al [3] explore contour and stroke fragments, Li et al [5] apply convolutional neural networks in combination with optical character recognition.…”

Section: Related Workmentioning

confidence: 99%

“…He et al [3] and Li et al [5] address the task of estimating the age of historical documents. While He et al [3] explore contour and stroke fragments, Li et al [5] apply convolutional neural networks in combination with optical character recognition. Ginosar et al [2] and Salem et al [9] model the differences of human appearance and clothing style in order to predict the date of photos in yearbooks.…”

Section: Related Workmentioning

confidence: 99%

“…But date estimation is an interesting and challenging task for historians, archivists, and even for sorting (digitized) personal photo collections chronologically. Existing approaches either rely on datasets solely containing historical color images [1,6,7] or focus on specific concepts like cities [10], cars [4], persons [2,9], or historical documents [3,5] and are therefore unable to learn the temporal differences of the broad variety of motives. For this reason, a huge dataset covering all kinds of concepts is necessary, which additionally enables the training of convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

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“When Was This Picture Taken?” – Image Date Estimation in the Wild

Müller

Springstein

Ewerth

2017

Lecture Notes in Computer Science

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Abstract. The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans.

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