Deep Learning Approaches for Textbook Recognition and Classification
- Resource Type
- Conference
- Authors
- Suryawanshi, P B; Singh Aswal, Upendra; Merikapudi, Seshaiah; Vekariya, Vipul; Patil, Harshal; Maranan, Ramya
- Source
- 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-6 Feb, 2024
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Adaptation models
Recurrent neural networks
Databases
Soft sensors
Robustness
Data models
Deep Learning
Textbook Recognition
Textbook Classification
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
- Language
- ISSN
- 2688-0288
In the digital era, the exponential growth of educational content has necessitated efficient methods for textbook recognition and classification. This paper explores the application of deep learning approaches to address the challenges associated with automating the recognition and categorization of textbooks. Leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our proposed model not only identifies and extracts textual information from images of textbooks but also classifies them into predefined categories. Experimental results demonstrate the efficacy of the proposed deep learning approach in achieving high accuracy in textbook recognition and classification tasks. The model's robustness is tested across diverse datasets, showcasing its adaptability to various educational domains and publication styles.