NDLI: Normalizing historical orthography for OCR historical documents using LSTM

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Normalizing historical orthography for OCR historical documents using LSTM

Content Provider	ACM Digital Library
Author	Breuel, Thomas M. Afzal, Muhammad Zeshan Azawi, Mayce Al
Abstract	Historical text presents numerous challenges for contemporary different techniques, e.g. information retrieval, OCR and POS tagging. In particular, the absence of consistent orthographic conventions in historical text presents difficulties for any system which requires reference to a fixed lexicon accessed by orthographic form. For example, language modeling or retrieval engine for historical text which is produced by OCR systems, where the spelling of words often differ in various way, e.g. one word might have different spellings evolved over time. It is very important to aid those techniques with the rules for automatic mapping of historical wordforms. In this paper, we propose a new technique to model the target modern language by means of a recurrent neural network with long-short term memory architecture. Because the network is recurrent, the considered context is not limited to a fixed size especially due to memory cells which are designed to deal with long-term dependencies. In the set of experiments conducted on the Luther bible database and transform wordforms from Early New High German (ENHG) $14^{th}$ - $16^{th}$ centuries to the corresponding modern wordforms in New High German (NHG). We compare our proposed supervised model LSTM to various methods for computing word alignments using statistical, heuristic models. Our new proposed LSTM outperforms the other three state-of-the-art methods. The evaluation shows the accuracy of our model on the known wordforms is 93.90% and on the unknown wordforms is 87.95%, while the accuracy of the existing state-of-the-art combined approach of the wordlist-based and rule-based normalization models is 92.93% for known and 76.88% for unknown tokens. Our proposed LSTM model outperforms on normalizing the modern wordform to historical wordform. The performance on seen tokens is 93.4%, while for unknown tokens is 89.17%.
Starting Page	80
Ending Page	85
Page Count	6
File Format	PDF
ISBN	9781450321150
DOI	10.1145/2501115.2501131
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Historical orthography Neural networks Normalization Long-short term memory Rule-based Ocr
Content Type	Text
Resource Type	Article

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