A Detailed Review on Text Extraction Using Optical Character Recognition

Thorat, Chhanam; Bhat, Aishwarya R; Sawant, Padmaja; Bartakke, Isha; Shirsath, Swati

doi:10.1007/978-981-16-5655-2_69

Cited by 20 publications

(13 citation statements)

References 8 publications

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“…Another study showed that automation led to a significant reduction in the time required for data extraction and improved the efficiency of the process, while also reducing the stress and workload on the individuals involved. [ 15 ] Several studies showed the use of the PyESAPI programming interface API in data extraction, treatment plan analysis, and automatic plan creation. [ 16 17 ]…”

Section: Discussionmentioning

confidence: 99%

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Chauhan

Pradhan

Munshi

et al. 2023

Journal of Medical Physics

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Background and Purpose: In recent years, data science approaches have entered health-care systems such as radiology, pathology, and radiation oncology. In our pilot study, we developed an automated data mining approach to extract data from a treatment planning system (TPS) with high speed, maximum accuracy, and little human interaction. We compared the amount of time required for manual data extraction versus the automated data mining technique. Materials and Methods: A Python programming script was created to extract specified parameters and features pertaining to patients and treatment (a total of 25 features) from TPS. We successfully implemented automation in data mining, utilizing the application programming interface environment provided by the external beam radiation therapy equipment provider for the whole group of patients who were accepted for treatment. Results: This in-house Python-based script extracted selected features for 427 patients in 0.28 ± 0.03 min with 100% accuracy at an astonishing rate of 0.04 s/plan. Comparatively, manual extraction of 25 parameters took an average of 4.5 ± 0.33 min/plan, along with associated transcriptional and transpositional errors and missing data information. This new approach turned out to be 6850 times faster than the conventional approach. Manual feature extraction time increased by a factor of nearly 2.5 if we doubled the number of features extracted, whereas for the Python script, it increased by a factor of just 1.15. Conclusion: We conclude that our in-house developed Python script can extract plan data from TPS at a far higher speed (>6000 times) and with the best possible accuracy compared to manual data extraction.

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Section: Discussionmentioning

confidence: 99%

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Chauhan

Pradhan

Munshi

et al. 2023

Journal of Medical Physics

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“…Thorat et al [69] presented a survey to discuss the methods used by previous researchers. It discussed OCR systems, tools, applications, phases, and methods.…”

Section: Segmentationmentioning

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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“…Optical Character Recognition (OCR) is used to convert documents into ASCII characters (machine-readable characters) [24]. In its application, OCR needs to perform an image transformation process first to allow machines to recognize characters just like humans use their eyes to see an image [25].…”

Section: Introductionmentioning

confidence: 99%

Text-Based Content Analysis on Social Media Using Topic Modelling to Support Digital Marketing

Buana,

Tyasnurita,

Puspita

et al. 2024

JOIV : Int. J. Inform. Visualization

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This study aims to create Social Media Analytics (SMA) tools to help Digital Marketers or Content Creators create content topics for creating text-based Instagram content and support digital marketing strategy. Since no SMA tools can provide topic discovery for text-based Instagram content, this research aims to make an SMA tool. The data requirements to make an SMA tool include content text, content caption text, likes, comments, upload time, and content category obtained through the Instascrapper. The method used in this study is the Topic Modelling method using the Latent Dirichlet Allocation (LDA) approach to find the most dominant topic in the content. Optical Character Recognition (OCR) performs an image transformation process to extract text from text-based Instagram content images. The results of SMA tool creation are tested on three expert users, which shows that 93% of test participants could use the SMA to find topic references, and 85% can still be used by users even though they find it difficult. Since the test result shows that SMA tools still need development, for further research, SMA tools can focus on developing the user experience to increase the value of user acceptance by paying attention to the ease of the SMA tools. Also, SMA tools can focus on target users such as Data Analysts, Business Intelligence Analysts, or others within a company to support decision-making for the marketing department.

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A Detailed Review on Text Extraction Using Optical Character Recognition

Cited by 20 publications

References 8 publications

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

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

Text-Based Content Analysis on Social Media Using Topic Modelling to Support Digital Marketing

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