Recognizing Handwritten Characters Using OCR & Converting into TTS

Yerrabolu Sailendra Chakravarthy, Rohit Singh, Manju More E

Abstract


The aim of the project is "To make Neural Networks aware of handwritten characters", that is, to create a platform that converts handwriting into digital text using Neural Network & Optical Character Recognition. This paper provides an in-depth study of text acquisition, tracking and image recognition with three major contributions. First, it is proposed that a standard framework for the release of image text that equally describes the discovery, tracking, recognition and their relationships and interactions. Second, within this framework, the various methods, systems, and procedures for visualizing the text of an image are summarized, compared, analyzed and the extracted text is converted and extracted by voice. Thirdly, related applications, outstanding challenges, and future directions for image editing are also well discussed.

 

 


Keywords


Handwritten Characters, Neural Network, Optical Character Recognition, OCR, TTS, OpenCV

Full Text:

PDF

References


Y. Zhu, C. Yao, and X. Bai, “Scene text detection and recognition: Recent advances and future trends,” Frontiers in Computer Science, vol. 10, no. 1, pp. 19-36,2019.

Oussama Zayene, Jean Hennebert, Sameh Masmoudi Touj, Rolf Ingold, and Najoua Essoukri Ben Amara. "A dataset for Arabic text detection, tracking and recognition in news videos-AcTiV." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 996-1000. IEEE, 2015.

Ruxandra Tapu, Bogdan Mocanu, and Titus Zaharia. "DEEP-SEE: Joint object detection, tracking and recognition with application to visually impaired navigational assistance." Sensors 17, no. 11 (2017): 2473.

Stanislav Mukhametshin, Alisa Makhmutova, and Igor Anikin. "Sensor tag detection, tracking and recognition for AR application." In 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1-5. IEEE, 2019.

Xu-Cheng Yin, Ze-Yu Zuo, Shu Tian, and Cheng-Lin Liu. "Text detection, tracking and recognition in video: a comprehensive survey." IEEE Transactions on Image Processing 25, no. 6 (2016): 2752-2773.

Taraggy M. Ghanim, Mahmoud I. Khalil & Hazem M. Abbas. Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition. IEEE, 10.1109/ACCESS.2020.2994290.

Ahmed Mahdi Obaid., IIHazem M. El Bakry., IIIM.A. Eldosuky., IVA.I. Shehab. Handwritten Text Recognition System Based on Neural Network. IJARCST, 4(1), 2347 - 8446, 2016.

Aisha Sharaf1., Bhagya Viswanath2., Kavya Chandran3., Nishana Salim4., Anju S Oommen5. Handwritten Text Recognition and Digitization System. IJIRSET, 8(5), 2319-8753, 2019.

J.Pradeep1 ., E.Srinivasan2., S.Himavathi3. Diagonal based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network. IJCSIT, 3(1), 2011.

Megha Agarwal., Shalika., Vinam Tomar., Priyanka Gupta. Handwritten Character Recognition using Neural Network and Tensor Flow. IJITEE, 8(6S4), 2278-3075, 2019.

Ashwinkumar.U.M and Dr. Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012

Tariq Rashid “Make Your Own Neural Network: A Gentle Journey Through the Mathematics of Neural Networks, and Making Your Own Using the Python Computer Language” CreateSpace Independent Publishing Platform, 2016




DOI: https://doi.org/10.26483/ijarcs.v12i0.6720

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 International Journal of Advanced Research in Computer Science