Malayalam Handwritten Words Recognition: A Review

Malayalam Handwritten Words Recognition: A Review

Aswin N S1, Fahsad T N 2, Sanath A S3, Sourav A V4, Ms. Geethu P C5

1,2,3,4Student, Department of computer science and engineering, Universal Engineering College, Kerala, India. 5Assistant Prof., Department of computer science and engineering, Universal Engineering College, Kerala, India.

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Abstract - This review examines character segmentation and offers an elegant method for identifying and transforming handwritten Malayalam words from picture documents into text. Character touchings, different writing styles, and noisy, damaged scanned photos make it difficult to recognise handwritten text. Taking use of today's world of rich data and algorithmic developments, the system uses deep convolutional neural networks (CNNs) to address these challenges. The three steps of Malayalam handwritten word recognition are segmentation, recognition, and pre-processing. Making Malayalam character datasets is the first stage, and then pre-processing to improve image quality comes next. Then, in order to maximise the system's capacity to precisely forecast Malayalam characters, a CNN model is built to extract relevant information. The last phase of the recognition process involves the system classifying the characters. This project is significant since it uses CNN filters to enhance feature recognition, which enhances the accuracy of Malayalam character prediction.

Key Words: Deep Learning, Deep Convolution Neural Network (DCNN), Character recognition, Character segmentation,