Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm

This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition), reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regards to HTR, this research demonstrates that even though automated transcription significantly increases the risk of text misclassification when compared to OCR, a cleanliness above ≈ 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution

Towards Tool Criticism: Complementing Manual with Computational Literary Analyses

Abstract of paper 0340 presented at the Digital Humanities Conference 2019 (DH2019), Utrecht , the Netherlands 9-12 July, 2019

Towards Tool Criticism: Complementing Manual with Computational Literary Analyses

Abstract of paper 0340 presented at the Digital Humanities Conference 2019 (DH2019), Utrecht , the Netherlands 9-12 July, 2019

The Grimm Brothers : a stylometric network analysis

Stylometric methods can be used to reveal similarities between texts and, combined with network analysis, to depict the stylistic relations between those texts. The research conducted here focuses on a corpus of letters written by Jacob and Wilhelm Grimm. Using stylometric analysis, we model the writing styles of the brothers depending on the addressees and chronology. The brothers have individual styles: Wilhelm has a more friendly and personal tone independent on addresses, while Jacob has a more impersonal style, unless he was writing to Wilhelm. Their styles merge at the interactions of their career or personal development

Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm

This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition), reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regards to HTR, this research demonstrates that even though automated transcription significantly increases the risk of text misclassification when compared to OCR, a cleanliness above 48 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution

Szerzőazonosítás Jacob és Wilhelm Grimm zajos, digitalizált levelezésében

Az alábbi cikk egy multidiszciplináris projekt eredményeit mutatja be, amely a különböző digitalizációs stratégiák számítógépes szöveganalízisben való használhatóságát járja körül. Pontosabban Jacob és Wilhelm Grimm szerzőségének automatizált megkülönböztetésére tettünk kísérletet, melyet egy HTR (Handwritten Text Recognition – kézzel írott szöveg felismerése) és OCR (Optical Character Recognition – optikai karakterfelismerés) által feldolgozott levelezéskorpuszban hajtottunk végre, korrekció nélkül – felmérve, hogy az így keletkezett zaj milyen hatással van a fivérek különböző írásmódjának azonosítására. Összegezve, úgy tűnik, hogy az OCR megbízható helyettesítője lehet a manuális átírásnak, legalábbis a szerzőazonosítás kérdéskörét illetően. Eredményeink továbbá abba az irányba mutatnak, miszerint még a különböző digitalizációs eljárásokból származó tanító- és tesztkorpuszok (training and test set) is használhatók a szerzőazonosítás során. A HTR-t tekintve a kutatás azt demonstrálja, hogy ez az automatizált átírás ugyan az OCR-hez képest szignifikánsan növeli a szövegek félrecsoportosításának veszélyét, ám körülbelül 20% feletti tisztaság már önmagában elegendő ahhoz, hogy a véletlennél nagyobb esélye legyen a helyes bináris megfeleltetésnek

Table_2.csv

<p>This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition), reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regards to HTR, this research demonstrates that even though automated transcription significantly increases the risk of text misclassification when compared to OCR, a cleanliness above ≈ 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution.</p

Table_1.csv

<p>This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition), reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regards to HTR, this research demonstrates that even though automated transcription significantly increases the risk of text misclassification when compared to OCR, a cleanliness above ≈ 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution.</p