An open-source OCR evaluation tool

The National Library of Finland (NLF) has digitized historical newspapers, journals and ephemera published in Finland since the late 1990s. The present collection consists of about 16.51 million pages mainly in Finnish and Swedish. Out of these about 7.64 million pages are freely available on the web site https:/ / digi.kansalliskirjasto.f / etusivu. The copyright restricted part of the collection can be used at six legal deposit libraries in different parts of Finland. The time period of the open collection is from 1771 to 1929.

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“…Cf. also Märgner and El Abed (2014) and Carrasco, (2014). 6 This version was produced by the subcontractor when the GT data was formed.…”

Section: Notesmentioning

confidence: 99%

Ground Truth OCR Sample Data of Finnish Historical Newspapers and Journals in Data Improvement Validation of a re-OCRing Process

2020

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“…Furthermore, we prioritize the improvement on the standard WER over its positional-independent counterpart, with the intent to preserve the syntactic and semantic attributes of the extracted text, and support meaningful Natural Language Processing and Topic Evolution analyses of the obtained documents. To record these three metrics, we used the ocrevalUAtion open source tool [24]. Besides the described metrics, this tool also reports error rates by character, and aligned bitext for each document match, facilitating the comparison of the generated output against the reference text [24].…”

Section: E Extracted Text Evaluationmentioning

confidence: 99%

“…To record these three metrics, we used the ocrevalUAtion open source tool [24]. Besides the described metrics, this tool also reports error rates by character, and aligned bitext for each document match, facilitating the comparison of the generated output against the reference text [24]. We set up the evaluation so that punctuation and case differences were counted as well, when calculating the scores.…”

Section: E Extracted Text Evaluationmentioning

confidence: 99%

Text extraction and retrieval from smartphone screenshots

Chiatti

Cho

Gagneja

et al. 2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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Daily engagement in life experiences is increasingly interwoven with mobile device use. Screen capture at the scale of seconds is being used in behavioral studies and to implement "just-in-time" health interventions. The increasing psychological breadth of digital information will continue to make the actual screens that people view a preferred if not required source of data about life experiences. Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data. In this paper, we present the experimental workflow we exploited to: (i) pre-process a unique collection of screen captures, (ii) extract unstructured text embedded in the images, (iii) organize image text and metadata based on a structured schema, (iv) index the resulting document collection, and (v) allow for Image Retrieval through a dedicated vertical search engine application. The adopted procedure integrates different open source libraries for traditional image processing, Optical Character Recognition (OCR), and Image Retrieval. Our aim is to assess whether and how state-of-the-art methodologies can be applied to this novel data set. We show how combining OpenCV-based pre-processing modules with a Long short-term memory (LSTM) based release of Tesseract OCR, without ad hoc training, led to a 74% character-level accuracy of the extracted text. Further, we used the processed repository as baseline for a dedicated Image Retrieval system, for the immediate use and application for behavioral and prevention scientists. We discuss issues of Text Information Extraction and Retrieval that are particular to the screenshot image case and suggest important future work.

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“…We extend our research to measure the impact of Combined Vertical Projection to the accuracy of OCR. The OCR accuracy is measured in Character Error Rate (CER), Word Error Rate (WER), and WER with Order Independent [8]. The computational time needed is also measured in 3 different iteration of Combined Vertical Projection technique.…”

Section: Introductionmentioning

confidence: 99%

Skew detection based on vertical projection in latin character recognition of text document image

Susanto¹,

Putri²,

Wiratama³

2018

IJET

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The accuracy of Optical Character Recognition is deeply affected by the skew of the image. Skew detection & correction is one of the steps in OCR preprocessing to detect and correct the skew of document image. This research measures the effect of Combined Vertical Projection skew detection method to the accuracy of OCR. Accuracy of OCR is measured in Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent). This research also measures the computational time needed in Combined Vertical Projection with different iteration. The experiment of Combined Vertical Projection is conducted by using iteration 0.5, 1, and 2 with rotation angle within -10 until 10 degrees. The experiment results show that the use of Combined Vertical Projection could lower the Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent) up to 35.53, 34.51, and 32.74 percent, respectively. Using higher iteration value could lower the computational time but also decrease the accuracy of OCR.

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An open-source OCR evaluation tool

Cited by 24 publications

References 10 publications

Ground Truth OCR Sample Data of Finnish Historical Newspapers and Journals in Data Improvement Validation of a re-OCRing Process

Ground Truth OCR Sample Data of Finnish Historical Newspapers and Journals in Data Improvement Validation of a re-OCRing Process

Text extraction and retrieval from smartphone screenshots

Skew detection based on vertical projection in latin character recognition of text document image

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