Multi-level Skew Correction Approach for Hand Written Kannada Documents

Vinod, H. C.; Niranjan, S. K.

doi:10.1007/978-3-319-73450-7_36

Cited by 4 publications

(1 citation statement)

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“…The applications of DIAR are ubiquitous in our daily endeavor, as evidenced on several areas of document image enhancement such as binarisation (Tensmeyer & Martinez, 2017), skew correction (Vinod & Niranjan, 2018) and text extraction (Saabni, Asi & El-Sana, 2014). DIAR bears an imperative role in document salvaging tasks namely document image and information retrieval (Ahmed, Al-Khatib & Mahmoud, 2017), old and historical manuscripts (Saabni et al, 2014), digital libraries (Abidi, Siddiqi & Khurshid, 2011), and information extraction from images (Chen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Automatic Multi-lingual Script Recognition Application

Abu-Ain

Abdullah

Omar

et al. 2018

GEMA

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Document Image Analysis and Recognition (DIAR) technique is used to recognize text component and translate it into editable format. Scripts are a set of graphical representations used to express a particular writing system as well as subsets belonging to a particular writing system. The writing styles of more than one script family may then be adopted by one language, such as in the cases where the old Malay language (Jawi) adopts the Arabic script while the modern one adopts the Roman script. The seven major scripts used in this research are in handwritten style including Arabic, Devanagari, Hebrew, Thai, Greek, Cyrillic and Korean. Automatic Multilingual Script Recognition (AMSR) is one of the main challenges in DIAR domain. Currently, only few attempts have been made for automated script identification of off-line handwritten documents images. Most available AMSR applications only deal with printed documents and script types, and they neglect handwritten and multilingual documents. The objective of this study is to propose a multilingual AMSR framework. The research methodology consists of a proposed multilingual AMSR framework. The multilingual AMSR framework is tested on Multilingual-HW datasets, which contains more than seven international unconstraint handwritten scripts, using Grey-Level Co-occurrence Matrix and Local Binary Pattern. The average accuracy of both methods is about 97.01% and 85.29% respectively. This proposed multilingual AMSR is hoped to be beneficial to a group of community which requires automatic sorting multilingual documents. This research can also be extended to document forensic area or international relations agency to identify unknown native document.

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