An Adaptive Foreground-Background Separation Method for Effective Binarization of Document Images

Das, Bishwadeep; Bhowmik, Showmik; Saha, Aniruddha; Sarkar, Ram

doi:10.1007/978-3-319-60618-7_51

Cited by 5 publications

(1 citation statement)

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“…As we are considering real-world handwritten documents, we need to be very careful about the noise present in these documents, which might affect the binarization and BB estimation process. Thus, for effective binarization, a background estimation and separation procedure is followed, prior to the actual binarization, using Otsu's method as given in [27]. During BB estimation from the binarized image, only the CCs having height and width greater than three pixels are considered to avoid noise.…”

Section: Methodsmentioning

confidence: 99%

Text/Non-Text Separation from Handwritten Document Images Using LBP Based Features: An Empirical Study

et al. 2018

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Isolating non-text components from the text components present in handwritten document images is an important but less explored research area. Addressing this issue, in this paper, we have presented an empirical study on the applicability of various Local Binary Pattern (LBP) based texture features for this problem. This paper also proposes a minor modification in one of the variants of the LBP operator to achieve better performance in the text/non-text classification problem. The feature descriptors are then evaluated on a database, made up of images from 104 handwritten laboratory copies and class notes of various engineering and science branches, using five well-known classifiers. Classification results reflect the effectiveness of LBP-based feature descriptors in text/non-text separation.

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Section: Methodsmentioning

confidence: 99%