Recognizing Call Numbers for Library Books: The Problem and Issues

Pham, Hanh; Dvorak, Andre; Pérez, Miguel Ángel Quiñones

doi:10.1109/csci46756.2018.00079

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…There have been a number of OCR algorithms developed over the years, stretching back to the 1980s and 1990s, with a particular focus on machine learning approaches (Burr, 1988;Matan et al, 1992;Lecun et al, 1995;Kim and Govindaraju, 1997). Work has also focused particularly on number recognition (Leelasantiham, 2009;Babbar et al, 2018;Pham et al, 2018), building digital libraries through the process of extracting bibliographic data and inventorying details from book images (Kashimura et al, 1999;Chen et al, 2010), vehicular license plate recognition (Babbar et al, 2018), traffic sign recognition (Mammeri et al, 2014), and credit card number digitization (Leelasantiham, 2009). All these methods involve a pipeline of preprocessing, thresholding, delineation of area of interest using a template before finally applying character recognition in the localized region.…”

Section: Background On Number Digitizationmentioning

confidence: 99%

“…Commercial OCR tools have generally been optimized for scanner-captured documents rather than camera-captured documents (Liang et al, 2004). For example, current PDF OCR tools include Google Drive OCR, Nuance, Adobe Acrobat Reader, and Readiris (Canon) (Pham et al, 2018). Image-based OCR tools include Tesseract OCR (Tesseract, 2005), Abbyy Mobile OCR Engine, and mobile applications such as CamScanner and My Edison (Mammeri et al, 2014).…”

Section: Background On Number Digitizationmentioning

confidence: 99%

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CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

Kulkarni

Katebi

Valderrama

et al. 2021

Front. Artif. Intell.

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Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34–203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.

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