Script Characters Research Articles

Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.

Image segmentation of Komering script using bounding box

The development of deep learning technology is widely used for various purposes, including recognizing characters in a document. One of the scripts that can benefit from this deep learning technology is the Komering script, which is a local script in the South Sumatra region. However, there are challenges in reading documents written in this script, requiring a method to separate each character in a document. Therefore, there is a need for a technology that can automatically segment images of documents written in the Komering script. This research introduces an innovative technique for segmenting images of characters in documents that contain Komering script characters. The segmentation technique employs bounding box technology to separate each Komering script character, subsequently recognized by a pre-trained deep learning model. The bounding box approach imposes restrictions on the segmented object area. To recognize Komering characters, a deep learning model with a convolutional neural network (CNN) algorithm is employed.

Advancements in Modi Script Character Recognition: A Comprehensive Survey

Advancements in Modi Script Character Recognition: A Comprehensive Survey

‘손(手)’類 部首字 중심의 草書 字形 및 類似 字形에 관한 고찰

This paper studies the cursive script of characters such as 手(Hands), 攴(slaps), 攵(slaps), 支(supports), 又(hands), 爪(nails), 寸(short length), 厶(arms), 力(strength), 文(words), 戈(weapons) and compound characters.
 This paper compares these characters with similar shapes. The meaning and shape of ancient characters have influenced cursive writing. Just organize it briefly.
 Words(‘手’) express the shape of fingers and fists. The shape of cursive script is affected by this. The character(‘攴’, ‘攵’) represents the appearance of holding a stick in hand. ‘戈’ is a weapon used in war. So these words replaced each other in cursive script. The character(‘攴’) appears alternately with this character( ‘力’字, ‘又’字, ‘’字, ‘支’字, ‘弓’字, ‘攵’字, ‘文’字 ). One of these phenomena is that the characters are similar, which is why they appear in cursive script. This character(‘’字, ‘弓’字) is influenced by that character(‘乃’字, ‘’字, ‘’字) and appears in cursive script. The cursive script of this character(‘又’) has also been replaced by that character(‘寸’). Or multiple of these words(‘又’) have been weakened. The cursive strokes of this character(‘爪’) have weakened. Elaborate or express in two points.
 This word(‘寸’) has a curved tail and then narrates together with dots. Or change the position of the point to express it. This character(‘對’) divides the upper part of that character(‘丵’) into three dots according to the ancient script shape. This character(‘厶’) is an old version of this character(‘厷’). This character(‘厷’) appears with that character(‘九’). The reason is that(‘九’) in ancient Chinese characters, this character(‘九’) presents the shape of a human arm, and that character(‘肱’) also presents the shape of a bent arm, so it appears alternately. Because these characters have a common curved shape, which character(‘厷’) is used instead of this one(‘弓’) to express. You can see the appearance of ‘厶’, ‘厷’, ‘弓’, ‘’ from the cursive script. This word(‘力’) expresses human muscles and tendons. These words(攵, 攴, 力) related to hands or arms are mixed together for use. This character(‘文’) also has a similar shape to that character(‘攵’), so it will also appear in cursive script. According to these contents, the characters are influenced by similar characters, ancient characters, and variant characters, appearing in cursive script characters. Exploring similar or ancient character forms can provide a lot of information for distinguishing cursive script forms. By using it, it is possible to provide rich explanations for more glyphs. I hope this will be of great help in understanding the shapes of cursive script.

‘손(手)’類 部首字 중심의 草書 字形 및 類似 字形에 관한 고찰

This paper studies the cursive script of characters such as 手(Hands), 攴(slaps), 攵(slaps), 支(supports), 又(hands), 爪(nails), 寸(short length), 厶(arms), 力(strength), 文(words), 戈(weapons) and compound characters.
 This paper compares these characters with similar shapes. The meaning and shape of ancient characters have influenced cursive writing. Just organize it briefly.
 Words(‘手’) express the shape of fingers and fists. The shape of cursive script is affected by this. The character(‘攴’, ‘攵’) represents the appearance of holding a stick in hand. ‘戈’ is a weapon used in war. So these words replaced each other in cursive script. The character(‘攴’) appears alternately with this character( ‘力’字, ‘又’字, ‘’字, ‘支’字, ‘弓’字, ‘攵’字, ‘文’字 ). One of these phenomena is that the characters are similar, which is why they appear in cursive script. This character(‘’字, ‘弓’字) is influenced by that character(‘乃’字, ‘’字, ‘’字) and appears in cursive script. The cursive script of this character(‘又’) has also been replaced by that character(‘寸’). Or multiple of these words(‘又’) have been weakened. The cursive strokes of this character(‘爪’) have weakened. Elaborate or express in two points.
 This word(‘寸’) has a curved tail and then narrates together with dots. Or change the position of the point to express it. This character(‘對’) divides the upper part of that character(‘丵’) into three dots according to the ancient script shape. This character(‘厶’) is an old version of this character(‘厷’). This character(‘厷’) appears with that character(‘九’). The reason is that(‘九’) in ancient Chinese characters, this character(‘九’) presents the shape of a human arm, and that character(‘肱’) also presents the shape of a bent arm, so it appears alternately. Because these characters have a common curved shape, which character(‘厷’) is used instead of this one(‘弓’) to express. You can see the appearance of ‘厶’, ‘厷’, ‘弓’, ‘’ from the cursive script. This word(‘力’) expresses human muscles and tendons. These words(攵, 攴, 力) related to hands or arms are mixed together for use. This character(‘文’) also has a similar shape to that character(‘攵’), so it will also appear in cursive script. According to these contents, the characters are influenced by similar characters, ancient characters, and variant characters, appearing in cursive script characters. Exploring similar or ancient character forms can provide a lot of information for distinguishing cursive script forms. By using it, it is possible to provide rich explanations for more glyphs. I hope this will be of great help in understanding the shapes of cursive script.

Qin Seal Script Character Recognition with Fuzzy and Incomplete Information

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.

A Study of Bear Totem Characters in Ancient Scripts: The Spread of Writings in the Hongshan Culture

A Study of Bear Totem Characters in Ancient Scripts: The Spread of Writings in the Hongshan Culture

YOLOv5 and U-Net-based Character Detection for Nusantara Script

Indonesia boasts a diverse range of indigenous scripts, called Nusantara scripts, which encompass Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese scripts. However, prevailing character detection techniques predominantly cater to Latin or Chinese scripts. In an extension of our prior work, which concentrated on the classification of script types and character recognition within Nusantara script systems, this study advances our research by integrating object detection techniques, employing the YOLOv5 model, and enhancing performance through the incorporation of the U-Net model to facilitate the pinpointing of fundamental Nusantara script's character locations within input document images. Subsequently, our investigation delves into rearranging these character positions in alignment with the distinctive styles of Nusantara scripts. Experimental results reveal YOLOv5's performance, yielding a loss rate of approximately 0.05 in character location detection. Concurrently, the U-Net model exhibits an accuracy ranging from 75% to 90% for predicting character regions. While YOLOv5 may not achieve flawless detection of all Nusantara scripts, integrating the U-Net model significantly enhances the detection rate by 1.2%.

The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models

Deep learning technology is widely used for recognizing character images, including various regional characters and diverse ancient scripts. Deep learning models require large data sets to recognize images accurately. However, creating a dataset has limitations in terms of quantity, including the Komering script dataset used in this study. Data augmentation techniques can be applied to expand the dataset by modifying existing images to increase data diversity. This study aims to investigate the impact of augmentation techniques on the performance of deep learning models in the case of Komering script recognition. The dataset consists of 500 images for five classes of Komering script characters. Three augmentation techniques, namely random rotation, height shift, and width shift, were applied to the five characters, which were then used to test the model trained to recognize characters in the Komering dataset. This research contributes to providing insights into the effect of augmentation techniques on robust confidence prediction of deep learning models for recognizing newly augmented data. The results demonstrate that the deep learning model can recognize modified data using augmentation techniques with an average accuracy of 80.05%.

Ranjana Script Handwritten Character Recognition using CNN

This paper proposes a public image database for Ranjana script Handwritten Character Datasets (RHCD), publicly available for Ranjana script researchers or anyone interested in the subject. To the best of our knowledge, the Ranjana script Handwritten Character Dataset (RHCD) is the first publicly available database for Ranjana script researchers. Ranjana script descended from the Brahmi script, consists of 36 consonant letters, 16 vowel letters, and 10 numerical letters. The focus of this research is three-fold: the first is to create a new database for Ranjana script Handwritten Character Recognition; the second is to test the character recognition accuracy of the created RHCD using existing CNN algorithms like LeNET-5, AlexNET, and ZFNET algorithm; the third is to propose a model by investigating different hyper-tuning parameters to improve the recognition accuracy of the created RHCD. The research method applied in this study is dataset collection, digitization & cropping, pre-processing, dataset splitting, data augmentation, and finally, implementing the CNN model (existing and proposed). Performance evaluation is based on the test accuracy, precision, recall, and F1-score. The experiment result shows that our model ranks first, with a testing accuracy of 99.73% for 64x64 pixels resolution with precision, recall, and F1-score value 1. Creation and recognition of Ranjana script characters, vowel modifiers, and compound characters can be the next milestone to be achieved. Segmentation of words and sentences into characters and recognizing each character individually can be the next research domain.

Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.

Classification of Ancient Modi Script Characters using Convolutional Neural Network

This study aims to develop a character recognition system for the MODI language, which is challenging due to the complexity of character identification. The proposed system uses convolutional neural networks (CNN) and the VGG16 algorithm to accurately identify printed and handwritten MODI characters from scanned papers, regardless of the input paper quality. The training dataset consists of 48 distinct MODI script characters, including vowels and consonants, and is routinely updated with handwritten samples obtained from various sources and the IEEE DataPort dataset. The study demonstrates the feasibility of developing a highly accurate character recognition system that follows the established method of the MODI Script Character Recognizer System (MSCR).

Offline MODI script character recognition using deep learning techniques

Offline MODI script character recognition using deep learning techniques

Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.

In defense of Ezra Pound’s “graphological” translations

ABSTRACT The Chinese translations of Ezra Pound, particularly those which feature his method of deconstructing individual Chinese characters, have frequently been criticised by scholars of sinology, and his translations have as such been classified as something ‘other’ (as ‘poet’s versions’ or ‘re-creations’) within translation studies. This paper attempts to reclaim Ezra Pound’s ‘etymographic’ translations as translation proper, at the same time widening the scope of the near-defunct term ‘graphological translation’. It will be shown that graphological translation can be performed when visual semantics are given priority over lexical semantics, and as such graphological translation cannot be judged by the successful (or otherwise) transfer of lexical meaning. It is easiest to approach Pound’s treatment of Chinese (as a semantically charged ‘motivated’ writing system) using his previously ignored translations out of the highly visual Naxi script, a minority writing of southwest Chinese, that can be seen in his late poetry notebooks. Pound treats both character scripts as essentially ideographic and in translation highlights the graphological semantics of the source writing.

Reflection on the Character Script of Contemporary Japanese Movies about the “Hikimori”

Reflection on the Character Script of Contemporary Japanese Movies about the “Hikimori”

Handwritten Balinese Script Recognition on Palm Leaf Manuscript using Projection Profile and K-Nearest Neighbor

This paper presents a simple approach to the handwritten Balinese script characters recognition in palm-leaf lontar manuscripts. The Lontar manuscript is one of the cultural heritages found in Bali. Lontar manuscripts are written using a pengrupak, which is a kind of knife for writing on palm leaves. To give color to the results of the writing, candlenut is used so that the writing appears clear. In this paper, we apply the projection profile at the segmentation stage to get the handwritten Balinese script characters in the lontar manuscript. The palm leaf manuscript that we use is the Wariga Palalubangan palm leaf. The recognition process is carried out by implementing K-Nearest Neighbor in the recognition process. The recognition was made on the Wianjana script obtained from lontar manuscripts using 720 images consisting of 18 classes as dataset training. The test results showed that the level of recognition accuracy was obtained by 52% in the characters of handwritten Balinese scripts derived from lontar manuscripts and 92% in the characters of handwritten Balinese scripts on paper.

Application Of Learning Vector Quantization Algorithm In Educational Games Learning To Write Aksara Bali

Almost every region in the nusantara has its own regional script, one of which is in Bali the people know the Balinese script as a script commonly used by their ancestors in writing. In order to keep this Balinese script sustainable and not extinct, learning about this script is applied in schools in Balinese language subjects. In addition to applying learning about script in schools, it would be better if learning about Balinese script could be done in more interesting ways and concepts. One of them is by making an educational game for the introduction of Balinese characters. Which in this application the user will provide input in the form of Balinese script characters written on the application, which then the input will go through a preprocessing process then proceed with diagonal feature extraction, then the results of the feature extraction will go through a classification process using the Learning Vector Quantization method. . The result is a web-based application that can recognize Balinese script writing using the Learning Vector Quantization classification method with an accuracy rate of 58.6% and get a good response from each respondent who has tested the application.

Anggah-Ungguhing Basa Bali dalam Pembelajaran Drama di Kelas X SMK Negeri 3 Tabanan Tahun Pelajaran 2019/2020

The skill of speaking in Balinese is closely related to the learning material of anggah-ungguhing basa Bali or sor singgih basa Bali. The use of Balinese language in vocational high schools can be taught through learning Balinese drama. The problems in this study are (1) the use of Balinese language in teaching drama in Class X SMK (Vocational High School) Negeri 3 Tabanan in the 2019/2020 academic year, (2) the obstacles faced in the use of Balinese language teaching in drama learning in the Class X SMK Negeri 3 Tabanan for the academic year 2019/2020, and (3) the strategies used to increase the use of Balinese language in teaching drama in Class X SMK Negeri 3 Tabanan for the academic year 2019/2020. This study concludes that in learning drama, students can use all types of anggah-ungguhing basa Bali, but there are still some errors in their use. Constraints experienced by students in the use of anggah - ungguhing basa Bali in drama learning are the lack of mastery of Balinese alus vocabulary, difficulty understanding characters in traditional Balinese drama scripts, and online learning systems. Learning strategies that can be used by teachers of Balinese language subjects in an effort to improve student's ability to understand the use of Balinese anggah - ungguhing basa in drama learning are innovative learning strategies that are integrated with elements of 21st century learning. The innovative learning strategies that can be developed by teachers including designing innovative learning, implementing innovative learning models, and utilizing digital learning.

Word Transcription of MODI Script to Devanagari Using Deep Neural Network

-Many historical documents and letters are written in MODI script. Study of “Shivakalin” and “Peshvekalin” era documents is almost impossible without the knowledge of MODI script. This work aims to bridge the gap between Devanagari and MODI Script by developing a system to map the recognized MODI characters to its Devanagari equivalent. Our dataset would comprise 57 different classes of MODI Script characters. We has solved the problem of loss of information in CNN as the model goes deeper using Dense Net-121. The various approaches for feature extraction usually used include moment invariant, affine moment invariant, chain code histogram, intersection junction and for character classification. Deep Neural Networks on the other hand do not require any feature to be explicitly defined, instead they work on the raw pixel data to generate the best features and use them to classify the inputs into different classes. Hence, we propose a deep learning architecture for character recognition. Dense Net-121 uses little pre-processing compared to other image classification algorithms. This means that the network learns the filters which in traditional algorithms were hand-engineered. The system aims to provide a good recognition rate by implementing DCNN. Key Words:Siamese Neural Network, CNN, Devanagari, HOCR 1.INTRODUCTION ( Size 11, Times New roman) Modi is an ancient script. Crores of Modi documents. Origin: 12th century and used uptil the 20th century. Modi lipi converter is a low resource solution Many historical documents and letters are written in MODI script. Study of “Shivakalin” and “Peshvekalin” era documents is almost impossible without the knowledge of MODI script. This work aims to bridge the gap between Devanagari and MODI Script by developing a system to map the recognized MODI characters to its Devanagari equivalent, provides authors with most of the formatting specifications needed for preparing electronic versions of their papers. All standard paper components have been specified for three reasons: (1) ease of use when formatting individual papers, (2) automatic compliance to electronic requirements that facilitate the concurrent or later production of electronic products, and (3) conformity of style throughout a conference proceedings. Margins, column widths, line spacing, and type styles are built-in; examples of the type styles are provided throughout this document and are identified in italic type, within parentheses, following the example. Some components, suchas multi-leveled equations, graphics, and tables are notprescribed, although the various table text styles are provided. The formatter will need to create these components, incorporating the applicable criteria that follow.