OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

Reul, Christian; Christ, Dennis; Hartelt, Alexander; Balbach, Nico; Wehner, Maximilian; Springmann, Uwe; Wick, Christoph; Grundig, Christine; Büttner, Andreas; Puppe, Frank

doi:10.3390/app9224853

Cited by 31 publications

(7 citation statements)

References 37 publications

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“…In addition to papers improving specific processes involved in OCR, some previous papers present a combination of various OCR techniques for overall improved process accuracy. OCR4all [109] is an open-source OCR software that combines state-of-the-art OCR components and continuous model training into a comprehensive workflow for processing historical printings and provides a user-friendly GUI and extensive configuration capabilities. The software outperforms commercial tools on moderate layouts.…”

Section: Discussionmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

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Section: Discussionmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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“…• Outdated datasets [27]: Outdated or obsolete datasets are another challenge to the existing datasets as most of these datasets contain document structure with an old format/layout. The datasets like RVL-CDIP contain document images with blur, obsolete format, and missing data values, creating problems during the information extraction process.…”

Section: Poor Qualitymentioning

confidence: 99%

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

2021

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The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and highquality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for endto-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multilayout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model. INDEX TERMS Artificial Intelligence (AI), information extraction, key field extraction, Named Entity Recognition (NER), template-free invoice processing, unstructured data.

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“…Data augmentation with artificial GT has been tested, without significant impact. Models have been produced with two different engines offering accessible user interfaces for non-specialists: Kraken [Kiessling 2019]/eScriptorium and Calamari [Wick et al 2018]/OCR4all [Reul et al 2019]. It has to be noted that scores are not strictly comparable (setups and evaluations are different), but both show extremely good results on the in-domain test set.…”

Section: Ocrmentioning

confidence: 99%