Rule-Based Page Segmentation for Palm Leaf Manuscript on Color Image

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

Because neural networks specialize in handling ambiguous data, they are especially suited for such applications as speech recognition and optical character recognition (OCR). OCR applications are usually ambiguous because their data is generated by an inconsistent factor—the individual. This article provides an overview of neural networks and describes how this technology can be integrated with OCR technology to create neural OCR networks that can significantly improve the process of optical character recognition.

Optical character recognition (OCR) is a vital process that involves the extraction of handwritten or printed text from scanned or printed images, converting it into a format that can be understood and processed by machines. The automatic extraction of text through OCR plays a crucial role in digitizing documents, enhancing productivity, and preserving historical records. This article offers an exhaustive review of contemporary applications, methodologies, and challenges associated with Arabic OCR. A thorough analysis is conducted on prevailing techniques utilized throughout the OCR process, with a dedicated effort to discern the most efficacious approaches that demonstrate enhanced outcomes. To ensure a thorough evaluation, a meticulous keyword-search methodology is adopted, encompassing a comprehensive analysis of articles relevant to Arabic OCR. In addition to presenting cutting-edge techniques and methods, this article identifies research gaps within the realm of Arabic OCR. We shed light on potential areas for future exploration and development, thereby guiding researchers toward promising avenues in the field of Arabic OCR. The outcomes of this study provide valuable insights for researchers, practitioners, and stakeholders involved in Arabic OCR, ultimately fostering advancements in the field and facilitating the creation of more accurate and efficient OCR systems for the Arabic language.

This paper presents a new color document image binarization that is suitable for palm leaf manuscripts and color document images. The proposed method consists of two steps: a pre-processing procedure using low-pass Wiener filter, and contrast adaptive binarization for segmentation of text from the background. Firstly, in the pre-processing stage, low-pass Wiener filter is used to eliminate noisy areas, smoothing of background texture as well as contrast enhancement between background and text areas. Finally, binarization is performed by using contrast adaptive binarization method in order to extract useful text information from low quality document images.The techniques are tested on a set of palm leaf manuscript images/color document images. The performance of the algorithm is demonstrated on by palm leaf manuscripts/color documents distorted with show-through effects, uneven background color and localized spot.

Text line extraction is one of the critical steps in document analysis and optical character recognition (OCR) systems. The purpose of this study is to address the problem of text line extraction of ancient Thai manuscripts written on palm leaves, using an Adaptive Partial Projection (APP) technique by integrating a modified partial projection and smooth histogram with recursion. The proposed approach was compared with a Modified Partial Projection (MPP) looking at vowel analysis and touching components of two consecutive lines. The results from this research suggested that the proposed approach for practical data on palm leaf manuscripts has better performance in solving the line segmentation problem.

More than two thousand years ago, the people of South Asia utilized palm leaf manuscripts for record-keeping and data transmission. These historical records contain value information for several age group peoples on a variety of themes, including culture, astronomy, mathematic, astrology and medicine. The valuable information is written in local languages these priceless records are damaged due to lack of maintained. Many researchers are dedicated to safeguarding the antique palm leaf scripts in order to preserve our priceless knowledge writings. However, as science and technology have advanced, images have become a vital means of transmitting information, and image processing has seen a surge in recent years. Numerous image processing techniques have been proposed for the efficient data retrieval, which includes image enhancement, segmentation, processing, restoration, compression and acquisition. Creating an effective image processing system to efficiently extract metadata from these manuscripts automatically is one of the goals. The world's oldest language is Tamil, and because writing styles vary greatly, it can be challenging from palm leaves to recognize ancient Tamil characters. Efficient feature extraction, selection, and character identification are necessary components of a system for identifying ancient Tamil characters. On this context, the study examines the literature on various techniques and strategies for locating, classifying, and extracting data from historical inscriptions inscribed on Tamil palm leaf manuscripts.

Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.

The advances in Optical Character Recognition (OCR) technology over the past decade have enabled the development of many automatic document-processing systems capable of 99% correct recognition on printed text. However, similar advances in Cursive Script Recognition (CSR) technology have not been forthcoming due, principally, to the vast variability of human handwriting. This paper investigates a method by which the more reliable OCR technology can be used to improve the CSR performance in a form processing application. A novel method is proposed to link handwriting data to contextual cue words that have been automatically obtained from an OCR process. This information is then used to select appropriate 'focused' lexicons to achieve better CSR results. The method was tested on 30 forms that were filled by 10 different writers. The experimental results together with a comparison to the base line recognition performance are presented.

Ancient palm leaf manuscripts in Thailand have been preserved by many organizations for the protection and retrieval of traditional knowledge. With advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these manuscripts. Binarization is an important stage during preprocessing of the manuscripts for subsequent extraction of text and characters. The output is then used for further processes such as character recognition and knowledge extraction. However, there is no single binarization technique that is suitable for all documents. This study aims to improve the binarization process intelligently by applying machine learning techniques to classify the optimal selection of binarization techniques of palm leaf manuscripts. Experiments results are reported and this technique could be applied for automatic selection of binarization techniques in other image processing problems.

The scanned image of the text is not of any use for user, because that image is not editable. One can not make any change if required to the scanned document. This provides a food for thought for the theory of optical character recognition (OCR). OCR is nothing but character recognition of a segmented part of the scanned image. Therefore the segmented part of the image would be such that it should provide a close relation to the character to be recognised. Hence segmentation plays an important role in the OCR process. There are problems in segmentation process, but the degree of the problems varies from script to script, that is, the problem set for segmentation of the text written in a particular script may differ than the problem set for the text written in other scripts. The characteristics of the script, plays a significant role in deciding the segmentation points. The present study is an effort to find these problems especially for the segmentation of text in Gurmukhi scripts – typed and handwritten.KeywordsOCRSegmentationCharacteristicsGurmukhiHandwrittenStyle

The scanned text image is a non editable image though it has the text but one can not edit it or make any change, if required, to that scanned document. This provides a basis for the optical character recognition (OCR) theory. OCR is the process of recognizing a segmented part of the scanned image as a character. The overall OCR process consists of three major sub processes like pre processing, segmentation and then recognition. Out of these three, the segmentation process is the back bone of the overall OCR process. We can say that the segmentation process is the most significant process because if the segmentation is incorrect then we can not have the correct results; it is just like garbage in and garbage out. But it is not an easy job, because segmentation is one of the complex processes. It is more difficult if the document is handwritten because in that case only few points are there which can be used to make segmentation. In this paper, we formulate an approach to segment the scanned document image. As per this approach, initially this considers the whole image as one large window. Then this large window is broken into less large windows giving lines, once the lines are identified then each window consisting of a line is used to find a word present in that line and finally to characters. For that purpose we used the concept of variable sized window, that is, the window whose size can be adjusted according to needs. This concept was implemented and results were analyzed. After the analysis the same concept was modified and finally tried on different documents and we got good reasonable results.

This research is a descriptive and explorative study and is aimed at defining the composition and indication of Thai traditional herbal medicinal preparations which were derived from the ancient Thai medicinal of palm leaf scriptures, chapter no.3, and volume 5. The research was investigated by the team of Mahasarakham University committee which consisted of pharmacist, biologists, interpreters and editors. The complete manuscripts contained 5 volumes in Thai Noi, Tham Esarn, khemer and Thai traditional languages. Each traditional medicinal herb diagnosed the symptoms of the patients and treats them with herbal preparations according to their local herbal medicinal knowledge and experiences in order to relieve various symptoms and diseases. These historic formulations were created over a period of hundreds of years before the advent of modern medicine. Key words : Palm leaf manuscripts; Therapeutic treatments; Thai Folk Herbalists

Abstract. This paper explores the application of biotechnology, specifically the Microbial Induced Calcium Precipitation (MICP) technology, in the protection of Palm Leaf Manuscripts in the Dai area of China. Through the perspective of a Dai student, it reveals the current situation and problems in the protection of Palm Leaf Manuscripts. By comprehensively analyzing the application cases of MICP technology at home and abroad and combining the characteristics of the palm leaf material used in Palm Leaf Manuscripts, this paper demonstrates the feasibility and effectiveness of the application of MICP technology and supplements the general data in the experimental procedures, providing a scientific and feasible solution for the protection of Palm Leaf Manuscripts.

In this work, we propose a character segmentation system from ancient palm leaf manuscripts written in ancient Arya Ezhuthu(which was popularized as script to write Malayalam from 17 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century). The automatic recognition of handwritten text from digital images of palm leaves dating back to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century BC. This can enable context based searching in large volumes of digital document. The subject about document image understanding is to extract and classify individual data meaningfully from palm leaf manuscript. We propose an automatic approach to detecting and identifying the characters. The proposed approach can improve the readability of the document. Text line segmentation is then applied and the characters are extracted.

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.