A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of these errors on natural language processing (NLP) tasks has only been partially studied. We perform a series of extrinsic assessment tasks — sentence segmentation, named entity recognition, dependency parsing, information retrieval, topic modelling and neural language model fine-tuning — using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks. We find a consistent impact resulting from OCR errors on our downstream tasks with some tasks more irredeemably harmed by OCR errors. Based on these results, we offer some preliminary guidelines for working with text produced through OCR.

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Assessing the Impact of OCR Quality on Downstream NLP Tasks

Arabic dialects also called colloquial Arabic or vernaculars are spoken varieties of Standard Arabic. These dialects have mixed form with many variations due to the influence of ancient local tongues and other languages like European ones. Many of these dialects are mutually incomprehensible. Arabic dialects were not written until recently and were used only in a speech form. Nowadays, with the advent of the internet and mobile telephony technologies, these dialects are increasingly used in a written form. Indeed, this kind of communication brought everyday conversations to a written format. This allows Arab people to use their dialects, which are their actual native languages for expressing their opinion on social media, for chatting, texting, etc. This growing use opens new research direction for Arabic natural language processing (NLP). We focus, in this paper, on machine translation in the context of Arabic dialects. We provide a survey of recent research in this area. We report for each study a detailed description of the adopted approach and we give its most relevant contribution.

/pdf/machine-translation-for-arabic-dialects-survey-uzwfo8coy8.pdf

Machine translation for Arabic dialects (survey)

Optical character recognition (OCR) is one of the most popular techniques used for converting printed documents into machine-readable ones. While OCR engines can do well with modern text, their performance is unfortunately significantly reduced on historical materials. Additionally, many texts have already been processed by various out-of-date digitisation techniques. As a consequence, digitised texts are noisy and need to be post-corrected. This article clarifies the importance of enhancing quality of OCR results by studying their effects on information retrieval and natural language processing applications. We then define the post-OCR processing problem, illustrate its typical pipeline, and review the state-of-the-art post-OCR processing approaches. Evaluation metrics, accessible datasets, language resources, and useful toolkits are also reported. Furthermore, the work identifies the current trend and outlines some research directions of this field.

https://dl.acm.org/doi/pdf/10.1145/3453476

Survey of Post-OCR Processing Approaches

ADAM: Analyzer for Dialectal Arabic Morphology

This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques. We argue that the main challenge for this task is that the OCR process leads to misspellings and linguistic errors in the output text. Moreover, historical variations can be present in aged documents, which can impact the performance of the NER process. We conduct a comparative evaluation on two historical datasets in German and French against previous state-of-the-art models, and we propose a model based on a hierarchical stack of Transformers to approach the NER task for historical data. Our findings show that the proposed model clearly improves the results on both historical datasets, and does not degrade the results for modern datasets.

/pdf/alleviating-digitization-errors-in-named-entity-recognition-19dgix1djt.pdf

Alleviating Digitization Errors in Named Entity Recognition for Historical Documents

The use of digital libraries requires an easy accessibility to documents which is strongly impacted by the quality of document indexing. Named entities are among the most important information to index digital documents. According to a recent study, 80% of the top 500 queries sent to a digital library portal contained at least one named entity [2]. However most digitized documents are indexed through their OCRed version which includes numerous errors that may hinder the access to them. Named Entity Recognition (NER) is the task that aims to locate important names in a given text and to categorize them into a set of predefined classes (person, location, organization). This paper aims to estimate the performance of NER systems through OCRed data. It exhaustively discusses NER errors arising from OCR errors; we studied the correlation between NER accuracy and OCR error rates and estimated the cost of character insertion, deletion and substitution in named entities. Results show that even if the OCR engine does contaminate named entities with errors, NER systems can overcome this issue and correctly recognize some of them.

/pdf/an-analysis-of-the-performance-of-named-entity-recognition-2qvaipatcn.pdf

An analysis of the performance of named entity recognition over ocred documents

Named entity processing over historical texts is more and more being used due to the massive documents and archives being stored in digital libraries. However, due to the poor annotated resources of historical nature, information extraction performances fall behind those on contemporary texts. In this paper, we introduce the development of the NewsEye resource, a multilingual dataset for named entity recognition and linking enriched with stances towards named entities. The dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 in French, German, Finnish, and Swedish. Such historical resource is essential in the context of developing and evaluating named entity processing systems. It evenly allows enhancing the performances of existing approaches on historical documents which enables adequate and efficient semantic indexing of historical documents on digital cultural heritage collections.

/pdf/a-multilingual-dataset-for-named-entity-recognition-entity-52iu2ixge6.pdf

A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers

Digital libraries are online collections of digital objects that can include text, images, audio, or videos. It has long been observed that named entities (NEs) are key to the access to digital library portals as they are contained in most user queries. Combined or subsequent to the recognition of NEs, named entity linking (NEL) connects NEs to external knowledge bases. This allows to differentiate ambiguous geographical locations or names (John Smith), and implies that the descriptions from the knowledge bases can be used for semantic enrichment. However, the NEL task is especially challenging for large quantities of documents as the diversity of NEs is increasing with the size of the collections. Additionally digitized documents are indexed through their OCRed version which may contains numerous OCR errors. This paper aims to evaluate the performance of named entity linking over digitized documents with different levels of OCR quality. It is the first investigation that we know of to analyze and correlate the impact of document degradation on the performance of NEL. We tested state-of-the-art NEL techniques over several evaluation benchmarks, and experimented with various types of OCR noise. We present the resulting study and subsequent recommendations on the adequate documents and OCR quality levels required to perform reliable named entity linking. We further provide the first evaluation benchmark for NEL over degraded documents.

/pdf/impact-of-ocr-quality-on-named-entity-linking-toy3c2zcsy.pdf

Impact of OCR Quality on Named Entity Linking

The development of natural language processing tools for dialects faces the severe problem of lack of resources. In cases of diglossia, as in Arabic, one variant, Modern Standard Arabic (MSA), has many resources that can be used to build natural language processing tools. Whereas other variants, Arabic dialects, are resource poor. Taking advantage of the closeness of MSA and its dialects, one way to solve the problem of limited resources, consists in performing a translation of the dialect into MSA in order to use the tools developed for MSA. We describe in this paper an architecture for such a translation and we evaluate it on Tunisian Arabic verbs. Our approach relies on modeling the translation process over the deep morphological representations of roots and patterns, commonly used to model Semitic morphology. We compare different techniques for how to perform the cross-lingual mapping. Our evaluation demonstrates that the use of a decent coverage root+pattern lexicon of Tunisian and MSA with a backoff that assumes independence of mapping roots and patterns is optimal in reducing overall ambiguity and increasing recall.

https://hal.archives-ouvertes.fr/hal-00908761/document

Ahmed Hamdi

Papers

An analysis of the performance of named entity recognition over ocred documents

Alleviating Digitization Errors in Named Entity Recognition for Historical Documents

A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers

Impact of OCR Quality on Named Entity Linking

The Effects of Factorizing Root and Pattern Mapping in Bidirectional Tunisian - Standard Arabic Machine Translation