Handprinted symbol recognition system

This article discusses the quality assessment of binary images. The customary, ground truth based methodology, used in the literature is shown to be problematic due to its subjective nature. Several previously suggested alternatives are surveyed and are also found to be inadequate in certain scenarios. A new approach, quantifying the adherence of a binarization to its document image is proposed and tested using six different measures of accuracy. The measures are evaluated experimentally based on datasets from DIBCO and H-DIBCO competitions, with respect to different kinds of binarization degradations.

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“…This problem was noted already in [14], where automatic systems were found to be more reliable than the human "ground truther".…”

Section: Quality Measurementioning

confidence: 93%

Beyond the Ground Truth: Alternative Quality Measures of Document Binarizations

Shaus

Sober

Turkel

et al. 2016

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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“…Computer researchers invented methodologies which transition an engineering drawing to digital forms. Brown et al (1988) and Joseph (1989) brought forth Optical Character Recognition (OCR) techniques which used Boolean logic-based symbol and numerical character recognition methods and line conversion methods to create the CAD system equivalents. An approach to distinguish the text and graphics used in an image was designed by Lu (1998) that differentiates by erasing non-text areas in an image.…”

Section: Related Workmentioning

confidence: 99%

Automatic Digitization of Engineering Diagrams using Intelligent Algorithms

Ghadekar¹,

Joshi²,

Swain³

et al. 2021

Journal of Computer Science

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At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.

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“…Since the 1980s, when the use of personal computers became widespread in the engineering design process, researchers have investigated many approaches to convert drawings to computer-aided digitalized documents. Brown et al [3] and Joseph [4] studied logical symbol and numerical character recognition systems that used optical character recognition (OCR) techniques and automated guided searching methods to convert line drawings to the CAD system format.…”

Section: Literature Reviewmentioning

confidence: 99%

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

Kang¹,

Baek²

2019

Energies

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In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.

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Handprinted symbol recognition system

Cited by 43 publications

References 0 publications

Beyond the Ground Truth: Alternative Quality Measures of Document Binarizations

Beyond the Ground Truth: Alternative Quality Measures of Document Binarizations

Automatic Digitization of Engineering Diagrams using Intelligent Algorithms

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

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