Symbol and character recognition: application to engineering drawings

Adam, Sébastien; Ogier, Jean-Marc; Cariou, Claude; Mullot, Rémy; Labiche, Jacques; Gardes, Joël

doi:10.1007/s100320000033

Cited by 78 publications

(35 citation statements)

References 76 publications

(58 reference statements)

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“…In 2000, one work on English stylistic text recognition is due to Adam et al [21] in which an approach of recognition of multi-oriented and multi-scaled character in engineering drawings is proposed. Fourier-Mellin transform is used to recognize the characters.…”

Section: Related Workmentioning

confidence: 99%

A novel method for straightening curved text-lines in stylistic documents

2014

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Stylistic text can be found on sign boards, street and organizations boards and logos, bulletin boards, announcements, advertisements, dangerous goods plates, warning notices, etc. In stylistic text images, text-lines within an image may have different orientations such as curved in shape or not be parallel to each other. As a result, extraction and subsequent recognition of individual text-lines and words in such images is a difficult task. In this paper, we propose a novel scheme for straightening of curved text-lines using the concept of dilation, flood-fill, robust thinning, and B-spline curve-based fitting. In the proposed scheme, at first, dilation is applied on individual text-lines to cover the area within a certain boundary. Next, thinning is applied to get the path of the text, approximate the path using the B-spline, find the angle between the normal at a point on the curve and the vertical line, and finally visit each point on the text and rotate by their corresponding angles. The proposed methodology is tested on variety of text images containing text-lines in Devanagari, English, and Chinese scripts which is evaluated on the basis of visual perception and the mean square error (MSE) calculation. MSE is calculated by line fitting applied on input and output images. On the basis of evaluation results obtained in our experiments, the proposed method is promising.

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Section: Related Workmentioning

confidence: 99%

A novel method for straightening curved text-lines in stylistic documents

2014

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“…However, these methods assume that the symbols or objects have been previously segmented as the case of (Tabbone et al, 2001). In order to avoid the use of ad-hoc segmentation strategies, works like (Adam et al, 2000) have proposed the use of digital filters applied for spotting purposes. In this paper the Fourier-Mellin transform is able to extract symbols and characters appearing in complete engineering drawings without segmentation.…”

Section: Related Workmentioning

confidence: 99%

Relational indexing of vectorial primitives for symbol spotting in line-drawing images

Rusiñol

Borrís

Lladós

2010

Pattern Recognition Letters

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This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an offthe-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.

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“…One type of research builds specific character recognition components for handling multioriented text labels [1,5], which differs from classic OCR research that assumes the documents containing text lines all in a single orientation. Deseilligny et al [5] use rotation-invariant features and Adam et al [1] use image features based on the Fourier-Mellin Transformation to compare the target characters with the trained character samples for recognizing text labels in maps. These methods require intensive training, such as providing sample characters for maps using different fonts to generate distinct feature sets for the classification.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Recognizing Text Labels in Raster Maps

Chiang

2010

2010 20th International Conference on Pattern Recognition

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Text labels in raster maps provide valuable geospatial information by associating geographical names with geospatial locations. Although present commercial optical character recognition (OCR) products can achieve a high recognition rate on documents containing text lines of the same orientation, text recognition on raster maps is challenging due to the varying text orientations and the overlap of text labels. This paper presents a text recognition approach that focuses on locating individual text labels in the map and detecting their orientations to then leverage the horizontal text recognition capability of commercial OCR software. We show that our approach detects accurate string orientations and achieves 96.2% precision and 94.7% recall on character recognition and 80.6% precision and 84.1% recall on word recognition.

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Symbol and character recognition: application to engineering drawings

Cited by 78 publications

References 76 publications

A novel method for straightening curved text-lines in stylistic documents

A novel method for straightening curved text-lines in stylistic documents

Relational indexing of vectorial primitives for symbol spotting in line-drawing images

An Approach for Recognizing Text Labels in Raster Maps

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