Stroke-Based Cursive Character Recognition

Santosh, K. C.; Iwata, Eizaburo

doi:10.5772/51471

Cited by 9 publications

(1 citation statement)

References 50 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to achieve this purpose, the development of a complete Document Image Processing System (DIPS) has become an utmost need. Along with the other steps, any DIPS needs to identify the texts present in a document image separately from the non-text components like tables, diagrams, graphic designs before processing the text through an Optical Character Recognition (OCR) engine [1][2][3]. The reason for this is very obvious: OCR engines do not process non-text components.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%