Automatic Handwritten Indian Scripts Identification

Pardeshi, Rajmohan; Chaudhuri, B. B.; Hangarge, Mallikarjun; Santosh, K. C.

doi:10.1109/icfhr.2014.69

Cited by 37 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Major handwritten datasets include IAM [286], NIST [271], MNIST [277], CEDAR [261], RIMES [313], [314], UNIPEN , CENPARMI-Arabic [340] PE92 [410], etc. The datasets developed are mostly in languages like English like IAM, CEDAR, NIST, MNIST, IAM-OnDB, etc., Arabic AHDB, ARABASE, CENPARMI-A, LMCA, KHATT, CENPARMI-F etc., Chinese HCL2000, CASIA, SCUT-COUCH, etc., Indian languages like Bangla: BN-HTRd, Numerals DB, Devanagari DB, Multiscript Indian DB (Bangla, Devanagari, Tamil, Telugu) [217], Multiscript DB 11 scripts (Roman, Devanagari, Urdu, Kannada, Oriya, Gujarati, Bangla, Gurumukhi, Tamil, Telugu, Malayalam ) [404] The traditional tasks for DAR, supported by most datasets, are pre-processing, segmentation and recognition. Other tasks like DLA, word spotting, and forensic document analysis (WI and verification) have very few datasets concerning them.…”

Section: Handwritten Datasetsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

View full text Add to dashboard Cite

<p>The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.</p>

show abstract

Section: Handwritten Datasetsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…So, designing a handwritten numeral recognizer which can work in a multilingual environment will be of huge significance in the Indian context. A lot of research work (Chatterjee et al, 2019;Hangarge et al, 2013;Obaidullah et al, 2019;Pardeshi et al, 2014;Sahare et al, 2019;Singh et al, 2019;Singh, Sarkar, Bhateja, & Nasipuri, 2018;Singh, Sarkar, Nasipuri, & Doermann, 2015) has already been done for the recognition of characters (considering only text), but only a few work (Obaidullah et al, 2015;Obaidullah et al, 2016;Rakshit et al, 2019;Roy et al, 2004) has been extended to numerals. Motivated by the research gap, our approach aims to recognize handwritten numerals in four major scripts used in the Indian sub-continent.…”

Section: Existing Workmentioning

confidence: 99%

A new feature extraction approach for script invariant handwritten numeral recognition

et al. 2021

View full text Add to dashboard Cite

Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi-lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script-invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

show abstract

“…Circularity is the most important feature among the mathematical feature and the fractal-based feature is the predominant feature among the structure based feature in [9]. In 2014, Rajmohan Pardeshi uses the feature based on spatial information and multi-resolution in [10], DCT coefficients of first 10 are preferred and added together to generate total 20 of features for script identification. Sub-band coding of DWT, projection of the RT, 46 dimensions of feature vector yields from SFs are used to compute standard deviation and entropy.…”

Section: Indian Handwritten Script Identification Systemmentioning

confidence: 99%

Indian Handwritten Script Identification System Based on Random Forest Tree Ensembles

Ganorkar¹,

Rojatkar²

2019

IJRTE

View full text Add to dashboard Cite

 Abstract: The work proposal addresses to introduce a methodology for Indian unconstrained handwritten script identification by practicing distinct features and classifiers. By utilizing classifiers like RF, SVM, k-NN, and LDA for Indian script identification using statistical, geometric, and structural features. To preserve all the information present on handwritten documents such as historical, medieval, inscription, financial administration, public records, government archives, letters, land councils, various agreements, etc. in digitalize form needs textual document processing system (e.g. OCR). To build a precise and productive multi-script/language textual document processing system must have script identification. For this study use, total 1288 (line wise) samples of ten scripts use in India are collected from different persons of different gender, age, education and region (rural or urban). After successful training and testing, 81.8% and 0.252 accuracies and the OOB error rate are achieved by Random Forest respectively. And 77.8%, 73.5%, and 65.5% accuracy is achieved in SVM, k-NN and LDA classifiers respectively.

show abstract

Automatic Handwritten Indian Scripts Identification

Cited by 37 publications

References 18 publications

Document Analysis and Recognition: A survey

Document Analysis and Recognition: A survey

A new feature extraction approach for script invariant handwritten numeral recognition

Indian Handwritten Script Identification System Based on Random Forest Tree Ensembles

Contact Info

Product

Resources

About