Affiliations: [a]
Department of Computer Science, School of Computing, Mysuru Campus, Amrita Vishwa Vidyapeetham, India
| [b]
Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
Correspondence:
[*]
Corresponding author. [*]N. Shobha Rani, Department of Computer Science, School of Computing, Mysuru Campus, Amrita Vishwa Vidyapeetham, India. E-mail: n_shobharani@my.amrita.edu.
Abstract: The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts.
Keywords: Document image classification, palm leaf manuscripts, handwritten document analysis, Tesseract OCR, deep learning, ancient document images
