Published Online:February 2025
Product Name:The IUP Journal of Operations Management
Product Type:Article
Product Code:IJOM010225
DOI:10.71329/IUPJOM/2025.24.1.5-18
Author Name:Sandeep Bhattacharjee
Availability:YES
Subject/Domain:Management
Download Format:PDF
Pages:5-18
Text detection is a key to real-time identification of packaged goods during transportation and warehousing. Text detection on packaged labels is the refined form of both identification and classification of packaged goods in transit. Algorithms in artificial intelligence are key tools that measure the degree of similarity or dissimilarity between different texts available on the label of packaged goods. With the growing number of complexities in text generation for varied results, the onus lies on the countering algorithms to detect similarities or dissimilarities and branch them or categorize them as per standard or customized requirements. Several such algorithms do exist to cater to such necessities. In this paper, three algorithms, namely, Cosine similarity algorithm, Jaccard similarity algorithm, and Levenshtein distance algorithm, have been tested using Python 3.10.13 codes on text generated by the user for conformity to such requirements.
Text detection is an important concept for identification and detection of similarities or dissimilarities in similar sets of texts. It is a measure of similarity or dissimilarity between one set of text and another.