Implementation of Levenshtein Distance Algorithm for ECommerce of Bravoisitees Distro

Umar, Rusydi; Hendriana, Yana; Budiyono, Eko

doi:10.14445/22312803/ijctt-v27p123

“…Levenshtein distance, also known as edit distance, is widely used for quantifying the dissimilarity of two text strings. This algorithm calculates the minimum amount of operations transforming a text string into another string, that includes replacement, deletion, and insertion [22] - [23]. For example, an optimal way to compare the two strings 'MONDAY' and 'SATURDAY', is to insert letters "S" and "A" and substitute "M", "O" and "N" with "T", "U" and "R", respectively, leading towards a Generalized Levenshtein Distance (GLD) of 5 [24].…”

Section: Levenshtein Distance For Text Similaritymentioning

confidence: 99%

DLI-IT: a deep learning approach to drug label identification through image and text embedding

Liu

¹

,

Meehan

²

,

Tong

³

et al. 2020

BMC Med Inform Decis Mak

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Background: Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. Methods: In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. Results:We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. Conclusion: To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

show abstract

“…Levenshtein distance, also known as edit distance, is widely used for quantifying the dissimilarity of two text strings. This algorithm calculates the minimum amount of operations transforming a text string into another string, that includes replacement, deletion, and insertion [23] - [25]. For example, an optimal way to compare the two strings 'MONDAY' and 'SATURDAY', is to insert letters "S" and "A"…”

Section: Levenshtein Distance For Text Similaritymentioning

confidence: 99%

DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

Liu

¹

,

Meehan

²

,

Tong

³

et al. 2020

Preprint

View full text Add to dashboard Cite

[Background] Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. [Methods] In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. [Results] We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. [Conclusion] To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

show abstract

“…Levenshtein distance, also known as edit distance, is widely used for quantifying the dissimilarity of two text strings. This algorithm calculates the minimum amount of operations transforming a text string into another string, that includes replacement, deletion, and insertion [23] - [25]. For example, an optimal way to compare the two strings 'MONDAY' and 'SATURDAY', is to insert letters "S" and "A" and substitute "M", "O" and "N" with "T", "U" and "R", respectively, leading towards a Generalized Levenshtein Distance (GLD) of 5 [26].…”

Section: Levenshtein Distance For Text Similaritymentioning

confidence: 99%

DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

Liu

¹

,

Meehan

²

,

Tong

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

[Background] Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. [Methods] In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. [Results] We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. [Conclusion] To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

show abstract

Implementation of Levenshtein Distance Algorithm for ECommerce of Bravoisitees Distro

Cited by 10 publications

References 0 publications

DLI-IT: a deep learning approach to drug label identification through image and text embedding

DLI-IT: a deep learning approach to drug label identification through image and text embedding

DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

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