Literature DB >> 36246341

A novel stock counting system for detecting lot numbers using Tesseract OCR.

Parkpoom Lertsawatwicha1, Phumidon Phathong1, Napatsorn Tantasanee1, Kotchakorn Sarawutthinun1, Thitirat Siriborvornratanakul1.   

Abstract

Counting stock is one of the warehouse's methods for preventing insatiable stock. Moreover, it could help the company forecast how many products they need to store and predict the replenished goods for customers. However, stock count in the medical business, which sells specialized medical equipment, needs more focus on, because it uses to treat the patient. So that lack of inventory should not happen. In a normal situation, stock count at some hospitals is quite hard for salespeople, especially hospitals in upcountry that far away. During the COVID-19 situation, many limits need to be strict. At this point, it causes a shortage of goods in many hospitals. In this paper, we represent how computer vision can help this process. When the hospital's officer sends images of stock to our system. The system will recognize the quantity and lot number of goods that remain in the hospital. Therefore, salespeople can decrease the times to visit hospitals. The result showed that for text detection and text recognition in a specific use case. Our prototype system achieves 84.17% in accuracy.
© The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Entities:  

Keywords:  Computer vision; Object detection; Optical Character Recognition; Tesseract

Year:  2022        PMID: 36246341      PMCID: PMC9540281          DOI: 10.1007/s41870-022-01107-4

Source DB:  PubMed          Journal:  Int J Inf Technol        ISSN: 2511-2104


  1 in total

1.  Text Detection and Recognition in Imagery: A Survey.

Authors:  Qixiang Ye; David Doermann
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2015-07       Impact factor: 6.226

  1 in total

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