On Evaluation of Segmentation-Free Word Spotting Approaches without Hard Decisions

Pantke, Werner; Märgner, Volker; Fingscheidt, Tim

doi:10.1109/icdar.2013.263

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The original methods work by locating the zones of interest through gradients, while the proposed method employs curvature according to a threshold instead. The resulting mAP based on the precision measures p IR and γ LA , presented in [115], showed that the proposed system outperformed the already existing application on the HADARA80P and the George Washington datasets.…”

Section: Hadara80pmentioning

confidence: 91%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

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This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 56 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or semantic analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

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Section: Hadara80pmentioning

confidence: 91%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Patnke et al [9] gave a detailed description of the HADARA dataset, including the structure, line and word segmentation, ground truth creation, and transcription of the pages, and integrated different template-based word spotting methods in their proposed method. A detailed evaluation procedure on segmentation-free word spotting methods on the HADARA dataset has been provided with equations and explanations [10].…”

Section: Training-free Methodsmentioning

confidence: 99%

Learning‐free handwritten word spotting method for historical handwritten documents

Hassen

Subramanian

Al-Madeed

2021

IET Image Processing

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Word spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi-language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross-correlation measure, and the extracted ROIs are reranked using feature extraction and matching methods. The algorithm handles two types of scenarios: Segmentation-based and segmentation-free. It also facilitates the search for words that occur once as well as multiple times in the image. Evaluations were conducted on the George Washington and HADARA datasets using a standard evaluation method. The proposed methodology shows improved performance over contemporary technologies currently being used in the word spotting research field.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A survey of historical document image datasets

Nikolaidou

Seuret

Mokayed

et al. 2022

IJDAR

View full text Add to dashboard Cite

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

show abstract

On Evaluation of Segmentation-Free Word Spotting Approaches without Hard Decisions

Cited by 6 publications

References 15 publications

A Survey of Historical Document Image Datasets

A Survey of Historical Document Image Datasets

Learning‐free handwritten word spotting method for historical handwritten documents

A survey of historical document image datasets

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