Evaluating and mitigating the impact of OCR errors on information retrieval

Oliveira, Lucas Lima de; Vargas, Danny Suarez; Alexandre, Antônio Marcelo Azevedo; Cordeiro, Fábio Corrêa; Gomes, Diogo da Silva Magalhães; Rodrigues, Max de Castro; Romeu, Regis Kruel; Moreira, Viviane Pereira

doi:10.1007/s00799-023-00345-6

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…Our work, despite the complexity of the topic, is presented in a language that is scientifically correct and does not lose the reader's interest. It is therefore suitable for all the following: (1) advanced researchers in OCR and deep learning who want to know the specific direction of the field, the challenges, and the problems that need to be researched further; (2) deep-learning and OCR practitioners who wish to know exactly which models to use and those that other practitioners have developed, in addition to possible applications for which solutions may be implemented; and (3) interested readers who are curious about the uses of deep learning in Arabic OCR and want to learn about the topic in a friendly way.…”

Section: Significance: Why and How Is This Work Different?mentioning

confidence: 99%

“…Optical character recognition (OCR) is a technique that is used to read and recognize the text present in an image and then convert it into a textual format. Once the text is extracted and digitized, it can utilize the applications of storing, retrieving, searching, and editing [1][2][3][4][5]. Interchangeably, OCR is referred to as "text recognition".…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Najam

Faizullah

2023

Applied Sciences

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Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing.

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Section: Significance: Why and How Is This Work Different?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Najam

Faizullah

2023

Applied Sciences

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“…A more recent work showed that information retrieval can be damaged with only 5% of error rate at the character-level [25]. Interestingly, de Oliveira et al [26] showed that, with comparable error rates in the documents, longer ones are more impacted by OCR errors than shorter ones.…”

Section: Related Workmentioning

confidence: 99%

A Quantitative Analysis of Noise Impact on Document Ranking

Giamphy,

Sanchis,

Dashyan

et al. 2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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After decades of massive digitization, a substantial amount of documents exists in digital form. The accessibility of these documents is strongly impacted by the quality of document indexing. Most of these documents are indexed in noisy versions that include numerous errors. The noise can be due to manual input mistakes or optical character recognition process and results in errors like spelling mistakes, missing characters, and others. This paper presents a study of the impact of noise on document ranking, an essential task in natural language processing (NLP) with wide-ranging practical applications. We provide a deep and quantitative analysis of the impact of recognition errors on document ranking by testing two popular ranking models on several noisy versions of a subset of the MS MARCO passage ranking dataset, with various levels and types of noise. Our study provides insights into the challenges of document ranking under noisy conditions and advocates for developing ranking models that are more robust to noise.

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“…Apart from the longer shelf-life of mathematics information, researchers preferred print because the medium was better suited to communicating equations and formulas. With the advancements of OCR technologies (i.e., accurate recognition and encoding of mathematical formulas and other special characters), mathematics information is more easily disseminated digitally today [6]. As a result, the Math Library observed a slower transition from print materials to a digital-preferred strategy than most STEM subject libraries, a phenomenon that will be speculated upon further in the Discussion section of this paper.…”

Section: Introductionmentioning

confidence: 89%