Precise conversion of inch and metric sizes on engineering drawings

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

Download Full-text

Enhancing CAD For The Forensic Engineer

Journal of the National Academy of Forensic Engineers ◽

10.51501/jotnafe.v2i1.389 ◽

1984 ◽

Vol 2 (1) ◽

Author(s):

Lindley Manning

Keyword(s):

Computer Graphics ◽

Photographic Evidence ◽

Computer Aided ◽

Engineering Drawings ◽

Forensic Engineering ◽

Selection Of

The purpose of this paper is to inform the Academy of an application of computer graphics that has been successful in the court room and which has the potential for extension to many related needs of the forensic engineer. An additional purpose is to examine the possibility of cooperation within the Academy to make a broad database and selection of equipment available to the members. Attentive engineers of today are well aware of the growing use and impact of computer-aided drafting, design and analysis in a wide variety of industries. In our field, we are aware of large analysis programs which have been used with success in court, for example the CRASH series. The authors forensic engineering partnership has developed ways to utilize the more widely available drafting systems to inexpensively fill the gap between photographic evidence and full engineering drawings. We have also found that CAD drawings appear to have more impact in court than hand done drawings. In some cases

Download Full-text

Multi-Oriented Text Extraction in Stylistic Documents

International Journal of Image and Graphics ◽

10.1142/s0219467815500023 ◽

2015 ◽

Vol 15 (01) ◽

pp. 1550002

Author(s):

Brij Mohan Singh ◽

Rahul Sharma ◽

Debashis Ghosh ◽

Ankush Mittal

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Morphological Operations ◽

Document Images ◽

Font Size ◽

Text Extraction ◽

Optical Character ◽

Engineering Drawings ◽

Flood Fill

In many documents such as maps, engineering drawings and artistic documents, etc. there exist many printed as well as handwritten materials where text regions and text-lines are not parallel to each other, curved in nature, and having various types of text such as different font size, text and non-text areas lying close to each other and non-straight, skewed and warped text-lines. Optical character recognition (OCR) systems available commercially such as ABYY fine reader and Free OCR, are not capable of handling different ranges of stylistic document images containing curved, multi-oriented, and stylish font text-lines. Extraction of individual text-lines and words from these documents is generally not straight forward. Most of the segmentation works reported is on simple documents but still it remains a highly challenging task to implement an OCR that works under all possible conditions and gives highly accurate results, especially in the case of stylistic documents. This paper presents dilation and flood fill morphological operations based approach that extracts multi-oriented text-lines and words from the complex layout or stylistic document images in the subsequent stages. The segmentation results obtained from our method proves to be superior over the standard profiling-based method.

Download Full-text