Alignment of Historical Handwritten Manuscripts Using Siamese Neural Network
| Autor: | Jumana Nassour, Majeed Kassis, Jihad El-Sana |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning 02 engineering and technology 010501 environmental sciences Spotting computer.software_genre 01 natural sciences Convolutional neural network Word recognition ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Affine transformation Artificial intelligence Hidden Markov model business computer Natural language processing Historical document 0105 earth and related environmental sciences |
| Zdroj: | ICDAR |
| DOI: | 10.1109/icdar.2017.56 |
| Popis: | Historical manuscript alignment is a widely known problem in historical document analysis, and the attempt of finding the differences between manuscript editions is mainly done by hand. Today, most of the computational tools coming to assist the historians are based on word recognition or spotting. These solutions are partial at best. In this paper, we present a Siamese neural network based system, which automatically identifies whether a pair of images contain the same text without the need of recognizing the text. The user is required to annotate several pages of two manuscripts, and with the assistance of synthetically generated data and affine distortions we can align two manuscripts written by different writers, achieving strong results. |
| Databáze: | OpenAIRE |
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