Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)
Priyanka Nirwan1, Gurpreet Singh2

1Ms. Priyanka Nirwan, Department of Computer Science Engineering, Chandighar University, (Punjab), India.
2Mr. Gurpreet Singh, Department of Computer Science Engineering, Chnadighar University, (Punjab), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 30 August 2019 | Manuscript Published on 16 September 2019 | PP: 603-607 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B11780782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1178.0782S619
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Abstract: Hand written text acknowledgment field has been considered as one of the hardest issues in the digital word. The multifaceted nature dimension of this exploration zone is high due to the reasons like diverse method for writing pursued by the clients, auxiliary independences, age elements of people and so on. This paper shows a novel procedure for the recognition of handwritten scripts, for example division of words and characters. In this paper, we have used two different scripts :”Devanagari” and “Roman” scripts. For which three Convolution Neural Networks(CNN) models are applied on different types of classification: one for language classification for which we have achieved 98% accuracy, second one for Devanagari character classification for which we have achieved 89% and third one for Roman character classification for which have achieved 97% respectively.
Keywords: Convolution Neural Network; Character Segmentation; Character Identification; Deep Learning; Handwritt En Text Recognition; Devanagari Script; Roman Script.
Scope of the Article: Deep Learning