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Utilising OpenCV with Tesseract to extract Bill of Materials (BOM) from Isometric Drawings

机译:利用opencv与tesseract以从等距图纸提取物料清单(BOM)

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Quality assurance is often a time-consuming and error prone process for organisations. However, it is increasingly important for companies that produce fabricated products for integration into safety critical environments. For example, creating pipe systems for the pharmaceutical industry will include additional risks. As a result, increased regulation is required, which has resulted in further paperwork and validation for companies operating in this sector.A lot of the isometric drawings provided to companies for fabrication remain in paper format (or scanned paper documents). This provides an administrative burden on these companies as the average project could generate up to 5,000 isometric drawings. This research explores techniques that could be utilised to automatically extract Bill of Materials (BOM) information from these isometric drawings.Tesseract has failed to perform OCR accurately on the extracted Region of Interest (ROI) data containing the BOM information, achieving a mean average of 43.8%. This paper explores different pre-processing techniques to increase the accuracy of recognition. Techniques such as binarisation, erosion, noise reduction and contouring were employed to increase this accuracy. In the study, the accuracy increased to a mean average of 81.2%. This has demonstrated that effective use of pre-processing can have an impact on character recognition.
机译:质量保证往往是一个耗时和错误的组织的过程。但是,对于生产制造产品的公司进行集成到安全关键环境中,越来越重要。例如,为制药行业创建管道系统将包括额外的风险。因此,需要增加的监管,这导致了在本扇区中运营的公司的进一步文书工作和验证。提供给制造公司的公司的大量等距图留在纸张格式(或扫描纸质文件)。这对这些公司提供了行政负担,因为平均项目可能会产生高达5,000个等距图纸。该研究探讨了可用于自动提取来自这些等距附图的材料(BOM)信息的技术。esSeract未能在包含BOM信息的提取的感兴趣区域(ROI)数据上准确地执行OCR,实现平均值43.8%。本文探讨了不同的预处理技术,以提高识别的准确性。使用诸如二核,侵蚀,降噪和轮廓等技术来提高这种准确性。在研究中,精度增加到平均平均值为81.2%。这已经证明,有效使用预处理可能对角色识别产生影响。

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