Grouping text lines in freeform handwritten notes

Ye, M.; Sutanto, Heri; Raghupathy, Sashi; Li, C.; Shilman, Michael

doi:10.1109/icdar.2005.121

Cited by 15 publications

(3 citation statements)

References 8 publications

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“…Shilman, etc. [4], [5] present an optimization method in freeform digital ink notes. A local gradient-descent algorithm iteratively evaluates splitting and merging hypotheses to minimize the global cost.…”

Section: Related Workmentioning

confidence: 99%

Text line extraction in free style document

et al. 2009

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This paper addresses to text line extraction in free style document, such as business card, envelope, poster, etc. In free style document, global property such as character size, line direction can hardly be concluded, which reveals a grave limitation in traditional layout analysis.'Line' is the most prominent and the highest structure in our bottom-up method. First, we apply a novel intensity function found on gradient information to locate text areas where gradient within a window have large magnitude and various directions, and split such areas into text pieces. We build a probability model of lines consist of text pieces via statistics on training data. For an input image, we group text pieces to lines using a simulated annealing algorithm with cost function based on the probability model.

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Section: Related Workmentioning

confidence: 99%

Text line extraction in free style document

et al. 2009

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“…The goal of the work presented by Liwicki et al [6] was very similar but did not handle non-textual strokes. Another study [7] presented the text line segmentation problem as stroke partitioning problem. In this work, the model complexity was optimized using gradient descent.…”

Section: Literature Surveymentioning

confidence: 99%

An approach based on classifier combination for online handwritten text and non-text classification in Devanagari script

et al. 2019

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In this article, a method of analysing features of elliptical regions and combining outcomes of classifiers using Dempster-Shafer Theory (DST) is presented to classify online handwritten text and non-text data of any online handwritten document in the most popular Indic script-Devanagari. Although a few works exist in this regard in different non-Indic scripts, to our knowledge, no study is available to classify handwritten text and non-text document in online mode in any Indic script. The present method uses various structural and directional features analysed in elliptical regions to extract feature values from strokes of text and non-text data. The features are then studied separately in classification platforms based on Support Vector Machine (SVM) and Hidden Markov Model (HMM). The probabilistic outcomes of these two classification platforms are then combined using DST to improve the system performance. The efficiency of the present system has been measured on a self-generated dataset and it provides promising result.

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“…We must look to the context of the sketch, with the domain in mind, to make specific context grouping decisions. Ye, Sutanto et al (2005) presents a novel way to group words, using a neighbourhood weighted graph and a cost function. The distances between each word is calculated, and the distance between the heights of the text strokes are used to give an indication of the probability of the text being on the same line.…”

Section: Ink Groupingmentioning

confidence: 99%