• Aditi Nene Department of Computer Engineering, RMDSinhgad School of Engineering
  • Aishawarya Palkar Department of Computer Engineering, RMD Sinhgad School of Engineering
  • Megha Nagarhalli Department of Computer Engineering, RMD Sinhgad School of Engineering
  • RichaMisri Department of Computer Engineering, RMD Sinhgad School of Engineering
  • Prof. ShivrajKone Department of Computer Engineering, RMD Sinhgad School of Engineering


Character Recognition by machines is an innovative way by which the dependence on manpower is reduced. Character recognition provides a reliable alternative of converting manual text into digitized format.Now-a-days, as technology becomes integral part of human life, many applications have enabled the incorporation of English OCR for real time inputs. The advantages that the English alphabet has is its simplicity offered by less number of lettersi.e. 26 and easier classification due to the concept of lowercase and uppercase. If we consider Devnagari script in this scenario, we will come across myriad hurdles because this script lacks the simplicity of English. The concept of fused letters, modifiers, shirorekha and spitting similarities in some letters make recognition difficult. Also, character recognition for handwritten text isfar more complex than that for machine printed characters. This is because of theversatility and different writing techniques adopted by people. The direction of strokes, pressure applied on writing equipments, quality of writing equipment and the mentality of the writer itself highly affects the written text. These problemswhen combined with the intricate details of Devnagari script, the complications in constructing a HCR of this script are increased.The proposed system focuses on these two issues byadopting Hough transform for detecting features from lines and curves. Further,for classification, SVM is used. These two methods when combinedprovide high accuracy which is up to 90%. Prior to these techniques, pre-processing ofcharacters is done to ensure accurate classification. This system is highly useful as it canbe used for automation of various services like postal, rail etc.


I. Poonam M. Ingle, P.P. Gumaste. Handwritten Devnagari Script Recognition by using Phase Correlation; 2014

II. Mr. Kuldeep P. Pawar, Mr. Digvijay J. Pawar, Mr. Yashwant S. Jagadale. A systematic approach to Devnagari Character Recognition method;2015

III. Pulkit Goyal, Sapan Diwakar, Anupam Agrawal. Devnagari Character Recognition towards Natural Human-Computer Interaction;2010

IV. Tanuja K, Usha Kumari V and Sushma T. M. Handwritten Hindi Character Recognition System using Edge Detection and Neural Network;2015

V. Parshuram M. Kamble, Ravinda S. Hegadib. Handwritten Marathi Character Recognition using R-HOG Feature;2015

VI. Prashant M. Kakde, Dr. S. M. Gulhane. A comparative analysis of particle swarm optimization and support vector machines for devnagari character recognition: An Android Application.;2016

VII. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, L. Malik, M. Kundu and D. K. Basu. Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition;2010

VIII. Ankita S. Wanchoo, Preet iYadav, Alwin Anuse. A survey on Devnagari Character Recognition for Indian Postal System Automation;2016

IX. Ms. Seema A. Dongare, Prof. Dhananjay B. Kshirsagar, Ms. Snehal V. Waghchaure. Handwritten Devnagari Character Recognition using Neural Network;2014

X. Dr. P. S, Deshpande, Latesh Malik, Sandhya Arora. Fine Classification and Recognition of Handwritten Devnagari Characters with Regular Expressions and Minimum Edit Distance Method;2008

XI. VedPrakash Agnihotri. Offline Handwritten Devnagari Script Recognition

XII. Asmita Kunkari. Optical Character Recognition System For Devnagari Script;2016

XIII. Shruti Agarwal, Dr. Naveen Hemarjani. Offline Handwritten Character Recognition with Devnagari Script;2013

XIV. Ankita Karia, Sonali Sharma, Reevon Rodrigues, Maitreya Save. Character Recognition ( Devnagari Script )

XV. Mitrakshi B. Patil, Vaibhav Narawade. Recognition of Handwritten Devnagari Characters through Segmentation and Artificial neural networks;2012.

Additional Files



How to Cite

Aditi Nene, Aishawarya Palkar, Megha Nagarhalli, RichaMisri, & Prof. ShivrajKone. (2017). SURVEY ON HANDWRITTEN DEVNAGARI CHARACTER RECOGNITION . International Education and Research Journal (IERJ), 3(3). Retrieved from