Script independent feature set for handwritten text recognition

Khanduja, Deepti; Nain, Neeta

doi:10.1109/mipro.2014.6859741

“…Further, they extracted 576 dimensional gradient features, achieved 8% and 12% false rejection rates, achieved 4% and 10% false acceptance rates, and achieved 6% and 11% average error rates for Hindi and English in each case, respectively. Next, Khanduja and Nain (2014) proposed a handwritten OCR through preprocessing, thinning, structural feature extraction, statistical feature extraction, simulation of best character features with quadratic curve fitting, and finally, back propagation learning based single layer feed forward ANN classification. Sahare and Dhok (2018) proposed a robust method to separate printed and handwritten multi-font text from noise contents and used multi-resolution and multi-directional properties of discrete contour-let transform and probabilitybased Moments features.…”

Section: Study I: a Review Of Bilingual Processing Systems For Englis...mentioning

confidence: 99%

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

¹

,

Singh

²

2020

International Journal of Applied Metaheuristic Computing

View full text Add to dashboard Cite

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

show abstract

Toward Recognition and Classification of Hindi Handwritten Document Image

Puri¹,

Singh²

2019

Advances in Intelligent Systems and Computing

2

0

View full text Add to dashboard Cite

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

¹

,

Singh

²

2022

Research Anthology on Bilingual and Multilingual Education

0

View full text Add to dashboard Cite

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

show abstract

Script independent feature set for handwritten text recognition

Cited by 3 publications

References 7 publications

Advanced Applications on Bilingual Document Analysis and Processing Systems

Advanced Applications on Bilingual Document Analysis and Processing Systems

Toward Recognition and Classification of Hindi Handwritten Document Image

Advanced Applications on Bilingual Document Analysis and Processing Systems

Contact Info

Product

Resources

About