Automatic text imprint analysis from pill images

Suntronsuk, Siroratt; Ratanotayanon, Sukanya

doi:10.1109/kst.2017.7886081

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…Experimental results showed that Otsu’s thresholding with noise elimination performed better than the K-mean clustering method. 86 Hence, the advantages of IE from images are efficiency, less complexity, and less time-consuming but when the image is noisy, one cannot take advantage without noise removal before IE. 87 In this manner, attention mechanism is the latest solution these days which uses encoder and decoder to detect, extract, and recognize the text for images.…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

“…Although images are rich container of information, certain challenges are also associated with IE from images. User-generated content on social media have variations in quality, 72,81,86,87 resolution, 81,82 and information representations. 82 Extracting useful information from these user-generated images is helpful as well as challenging.…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

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Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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“…Edge chart was used to find a bounding box that encompassed the major outline, which was the pill. They used tesseract for OCR after that (11) . For segmenting the pill image, Wang et al used the Modified Stroke Width and TSDS for describing the shape of the pill.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Shashidhar¹,

Sahana²,

Chakraborty³

et al. 2021

IJST

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Objectives: To create a tablet recognition system that recognizes and gives an audio output of the name of the tablet so that the visually impaired person may recognize it. Methods: In this, to create a user friendly system, such that a blind person does not require the help of another person to use it. These systems give medical assistive instrument to take the drug at the accurate time as per the doctor prescription and removes dependency on others for the visually challenged people. Various models were technologically advanced to compact with misidentification of remedies but are incompetent of formative exact pill picked by the person. Findings: The name of the medicine is obtained from the image. Then the name of the medicine is converted from text to speech using Google text to speech (gTTS). The proposed system will be helpful for visually challenged people, SQLite Database management system has been used along with Scale Invariant Feature Transform algorithm (SIFT) to identify and pronounce local features in the image. The image of the medicine given by the user is matched with the images in the database. Novelty: Developed a user-friendly voice based tablet recognition system for visually impaired people and this system is fast and accurate.

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“…Alternatively, image-based solutions have been developed. Traditional image recognition finds features through algorithms and then classifies images using certain classifiers [13,14]. Lee et al encoded color and shape into a three-dimensional histogram and geometric matrix, and encoded the imprint as a feature vector through a Scale Invariant Feature Transform (SIFT) descriptor and a Multi-scale Local Binary Pattern (MLBP) [15].…”

Section: Introductionmentioning

confidence: 99%

A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan

Ting

Chung

Chen

et al. 2020

BMC Health Serv Res

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Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them. Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the frontside model (93.72%). Conclusions:In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

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Automatic text imprint analysis from pill images

Cited by 9 publications

References 6 publications

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan

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