Finding straight lines in drawings

Arias, Juan F.; Chhabra, Atul K.; Misra, Vishal

doi:10.1109/icdar.1997.620618

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Several studies have been presented on table recognition [1], [2], [3], [4], [5], [6], [7], [8], [9]. Some of these researches use tables without imperfections in the horizontal and vertical line segments to reduce the complexity of the problem.…”

Section: Fig 1 Example Of a Table-form Documentmentioning

confidence: 99%

Handwritten Artefact Identification Method for Table Interpretation with Little Use of Previous Knowledge

Neves

Carvalho

Facon

et al. 2006

Document Analysis Systems VII

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An artefact identification method for handwritten filled table-forms is presented. Artefacts in table-forms are smudges and overlaps between handwritten data and line segments which increase the complexity of table-form interpretation. After reviewing some knowledge-based methods, a novel artefact identification method to improve table-form interpretation is presented. The proposed method aims to detect, identify and remove table-form artefacts with little use of previous knowledge. Experiments show the significance of using the proposed artefact identification method to improve table-form interpretation rates.

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Section: Fig 1 Example Of a Table-form Documentmentioning

confidence: 99%

Handwritten Artefact Identification Method for Table Interpretation with Little Use of Previous Knowledge

Neves

Carvalho

Facon

et al. 2006

Document Analysis Systems VII

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“…As a line is always just a few pixels thick and is usually much thinner than text or other entities, a line Although the line-searching algorithm just described may seem rather simple to be able to identify all of the possible lines in a document image, the method does provide a quick solution for identifying long lines in non-complex documents such as form documents. It is by no means a robust method for finding lines in complex documents when compared to other methods reported in literature [47,48,49], however, the main advantage of using such a method is its ease of implementation. Besides, the main reason for finding lines in this case is to perform subtraction at a later stage.…”

Section: Form Structure Analysis and Identificationmentioning

confidence: 99%

Use of colour for hand-filled form analysis and recognition

Sherkat

Allen

Wong

2005

Pattern Anal Applic

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Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images.Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system.

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“…The algorithm works directly on run-length encoded images, and is very fast. It was later extended to detect lines with any orientation after implementing an efficient rotation operations on run-length coded images [18]. Recently, a Directional Singly Connected Chain (DSCC) method was proposed in [1].…”

Section: Related Work 21 Line Detectionmentioning

confidence: 99%

“…This property can be used to develop an efficient algorithm by detecting intersections of horizontal and vertical lines first. The verification of line segments between intersections complete the algorithm [6], [18].…”

Section: Related Work 21 Line Detectionmentioning

confidence: 99%

A parallel-line detection algorithm based on HMM decoding

Zheng

Doermann

2005

IEEE Trans. Pattern Anal. Machine Intell.

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The detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction from rule lined paper. These tasks can be very challenging in degraded documents where the lines are severely broken. In this paper, we propose a novel model-based method which incorporates high-level context to detect these lines. After preprocessing (such as skew correction and text filtering), we use trained Hidden Markov Models (HMM) to locate the optimal positions of all lines simultaneously on the horizontal or vertical projection profiles, based on the Viterbi decoding. The algorithm is trainable so it can be easily adapted to different application scenarios. The experiments conducted on known form processing and rule line detection show our method is robust, and achieves better results than other widely used line detection methods.

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Finding straight lines in drawings

Cited by 9 publications

References 3 publications

Handwritten Artefact Identification Method for Table Interpretation with Little Use of Previous Knowledge

Handwritten Artefact Identification Method for Table Interpretation with Little Use of Previous Knowledge

Use of colour for hand-filled form analysis and recognition

A parallel-line detection algorithm based on HMM decoding

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