Identification of business forms using relationships between adjacent frames

Lin, Jenn-Yih; Lee, Chi‐Wei; Chen, Zen

doi:10.1007/s001380050028

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…In [47], they detect frame vertices and lines by applying Fan and Chang method to an adjacency matrix derived from the form's image. A graph is then constructed to model the form with each node representing a frame that contains filled in data.…”

Section: Segmentationmentioning

confidence: 99%

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Applications of Text Detection and its Challenges

Nevetha

Baskar

2015

Proceedings of the Third International Symposium on Women in Computing and Informatics

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The rising need for automation of systems has effected the development of text detection and recognition from images to a large extent. Text recognition has a wide range of applications, each with scenario dependent challenges and complications. How can these challenges be mitigated? What image processing techniques can be applied to make the text in the image machine readable? How can text be localized and separated from non textual information? How can the text image be converted to digital text format? This paper attempts to answer these questions in chosen scenarios. The types of document images that we have surveyed include general documents such as newspapers, books and magazines, forms, scientific documents, unconstrained documents such as maps, architectural and engineering drawings, and scene images with textual information.

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Section: Segmentationmentioning

confidence: 99%

“…Said, et al use a segmentation method using extended the Otsu's method for effective thresholding, in order to extract both machine printed and hand written text without much loss of information [34]. [47] proposes a method for identification of the category of a form based on its layout.…”

Section: Segmentationmentioning

confidence: 99%

Applications of Text Detection and its Challenges

Nevetha

Baskar

2015

Proceedings of the Third International Symposium on Women in Computing and Informatics

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“…There are many propositions for form processing available in the literature [11,8,13,14,9,6,5,2,3]. In most of the cases features related to lines and boxes are extracted.…”

Section: Past Workmentioning

confidence: 99%

A hierarchical method for automated identification and segmentation of forms

Mandal

Chowdhury

Das

et al. 2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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In this paper we propose a fully automatic hierarchical method for identification of forms using global as well as local features. Moments of certain orders are considered as global shape features and are utilised to reduce the search space by selecting a subset of forms present in the database. The type of the candidate form is then identified within this subset through detail analysis using local geometrical and topological features. The candidate form is then segmented to extract the user-filled information.

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“…Some methods for form identification which classify many form documents and identify a particular form type in a document database have been reported [11][12][13][14]. These methods use the layout of the form as the basis for identification.…”

Section: Introductionmentioning

confidence: 99%

“…These methods use the layout of the form as the basis for identification. Lin et al [11] adopted relationships between adjacent fields to identify forms. Fan et al [12] proposed a clusteringbased form identification method which characterises the topological and geometric structure of a form.…”

Section: Introductionmentioning

confidence: 99%

Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

Ishitani

2000

Pattern Analysis & Applications

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A new method of image understanding for forms based on model matching is proposed in this paper as the basis of an OCR which can read a variety of forms. The outline of this method is described as follows. First, ruled lines are extracted from the input image of a form. After that, several lines are grouped as one to be recognised as data corresponding to a sub-form. These lines and subforms are both used for understanding the form, taking into account their feature attributes and the relationships between them. Each feature in the input image of a form is expected to correspond to a feature in one of the model forms, which are described as structured features. This correspondence is represented by a node in an association graph, where an arc represents compatible correspondences established on the basis of feature relationships. The best match is found as the largest maximal clique in the association graph. Experimental results show the method is robust and effective for document images of poor quality, and also for various styles of forms.

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Identification of business forms using relationships between adjacent frames

Cited by 6 publications

References 5 publications

Applications of Text Detection and its Challenges

Applications of Text Detection and its Challenges

A hierarchical method for automated identification and segmentation of forms

Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

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