A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition

Joseph, Solley; George, Jossy P.

doi:10.1007/978-981-19-0095-2_34

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…While STV doesn't explicitly calculate tensor fields, it has the capability to generate them when necessary. The decay function for the STV kernel is illustrated in (5).…”

Section: A An Overview Of Tensor Voting Methods In Two-dimensional Spacementioning

confidence: 99%

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Direct Tensor Voting in line segmentation of handwritten documents

Babczyński,

Ptak

2024

International Journal of Electronics and Telecommunications

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In the vast archives and libraries of the world, countless historical documents are tucked away, often difficult to access. Thankfully, the digitization process has made it easier to view these invaluable records. However, simply digitizing them is not enough – the real challenge lies in making them searchable and computer-readable. Many of these documents were handwritten, which means they need to undergo handwriting recognition. The first step in this process is to divide the document into lines. This article introduces a solution to this problem using tensor voting. The algorithm starts by conducting voting on the binary image itself. Then, using the local maxima found in the resulting tensor field, the lines of text are precisely tracked and labeled. To ensure its effectiveness, the algorithm’s performance was tested on the data-set delivered by the organizers of the ICDAR 2009 competition and evaluated using the criteria from this contest.

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“…While STV doesn't explicitly calculate tensor fields, it has the capability to generate them when necessary. The decay function for the STV kernel is illustrated in (5).…”

Section: A An Overview Of Tensor Voting Methods In Two-dimensional Spacementioning

confidence: 99%

“…Moreover, you may find more recent reviews in the papers [4], [5]. In the first case, unfortunately, details of the experiments were not given.…”

Section: Background Researchmentioning

confidence: 99%