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Journal articles on the topic 'Identification du script'

Author: Grafiati

Published: 17 February 2023

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1

Ubul, Kurban, Gulzira Tursun, and Alim Aysa. "Recent Advances in Script Identification." Applied Mechanics and Materials 610 (August 2014): 734–40. http://dx.doi.org/10.4028/www.scientific.net/amm.610.734.

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There are a variety of different scripts in the world. Almost every country have there own languages and scripts which can distinguish from each other in different aspects. It is very essential to identify different scripts in multi-lingual, multi-script document. In recent years, different kinds of approaches have been developed for script identification and gotten promising results. In this paper, an overview of the script identification is proposed under different categories: script systems, extracted features and classification methods. Earlier researches and future property of this field is discussed. It is very obvious that, the research in this area is not so satisfied and still more research is to be done.

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2

Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Word-Level Script Identification Using Texture Based Features." International Journal of System Dynamics Applications 4, no. 2 (April 2015): 74–94. http://dx.doi.org/10.4018/ijsda.2015040105.

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Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

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Obaidullah, Sk Md, Chitrita Goswami, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (February 27, 2017): 1753003. http://dx.doi.org/10.1142/s0218001417530032.

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We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scripts: Roman and Urdu are considered as negative samples. With this precursor, an overall script identification performance can be advanced by more than 5.13% in accuracy and 1.17 times faster in processing time as compared to conventional system.

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Mahajan, Shilpa, and Rajneesh Rani. "Word Level Script Identification Using Convolutional Neural Network Enhancement for Scenic Images." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (July 31, 2022): 1–29. http://dx.doi.org/10.1145/3506699.

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Script identification from complex and colorful images is an integral part of the text recognition and classification system. Such images may contain twofold challenges: (1) Challenges related to the camera like blurring effect, non-uniform illumination and noisy background, and so on, and (2) Challenges related to the text shape, orientation, and text size. The present work in this area is much focused on non-Indian scripts. In contrast, Gurumukhi, Hindi, and English scripts play a vital role in communication among Indians and foreigners. In this article, we focus on the above said challenges in the field of identifying the script. Additionally, we have introduced a new dataset that contains Hindi, Gurumukhi, and English scripts from scenic images collected from different sources. We also proposed a CNN-based model, which is capable of distinguishing between the scripts with good accuracy. Performance of the method has been evaluated for own dataset, i.e., NITJDATASET and other benchmarked datasets available for Indian scripts, i.e., CVSI-2015 (Task-1 and Task 4) and ILST. This work is an extension to find the script from strict text background.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Line Parameter based Word-Level Indic Script Identification System." International Journal of Computer Vision and Image Processing 6, no. 2 (July 2016): 18–41. http://dx.doi.org/10.4018/ijcvip.2016070102.

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In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.

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6

Busch, A., W. W. Boles, and S. Sridharan. "Texture for script identification." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 11 (November 2005): 1720–32. http://dx.doi.org/10.1109/tpami.2005.227.

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7

Behrad, Alireza, Malike Khoddami, and Mehdi Salehpour. "A novel framework for Farsi and latin script identification and Farsi handwritten digit recognition." Journal of Automatic Control 20, no. 1 (2010): 17–25. http://dx.doi.org/10.2298/jac1001017b.

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Optical character recognition is an important task for converting handwritten and printed documents to digital format. In multilingual systems, a necessary process before OCR algorithm is script identification. In this paper novel methods for the script language identification and the recognition of Farsi handwritten digits are proposed. Our method for script identification is based on curvature scale space features. The proposed features are rotation and scale invariant and can be used to identify scripts with different fonts. We assumed that the bilingual scripts may have Farsi and English words and characters together; therefore the algorithm is designed to be able to recognize scripts in the connected components level. The output of the recognition is then generalized to word, line and page levels. We used cluster based weighted support vector machine for the classification and recognition of Farsi handwritten digits that is reasonably robust against rotation and scaling. The algorithm extracts the required features using principle component analysis (PCA) and linear discrimination analysis (LDA) algorithms. The extracted features are then classified using a new classification algorithm called cluster based weighted SVM (CBWSVM). The experimental results showed the promise of the algorithms.

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Lu, Liqiong, Dong Wu, Ziwei Tang, Yaohua Yi, and Faliang Huang. "Mining discriminative patches for script identification in natural scene images." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 551–63. http://dx.doi.org/10.3233/jifs-200260.

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This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

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9

Schellens, Tammy, Hilde Van Keer, Bram De Wever, and Martin Valcke. "The effects of two computer-supported collaborative learning (CSCL) scripts on university students' critical thinking." Psicologia Escolar e Educacional 11, spe (December 2007): 83–92. http://dx.doi.org/10.1590/s1413-85572007000300008.

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The present study focuses on the use of two different types of scripts as possible ways to structure university students' discourse in asynchronous discussion groups and consequently promote their learning. More specifically, the aim of the study is to determine how requiring students to label their contributions by means of De Bono's Thinking Hats (script 1) and Weinberger's script for the construction of argumentation sequences (script 2) affects the ongoing critical thinking processes reflected in the discussion. The results suggest that both scripts successfully facilitated critical thinking. The results showed that the labeling condition (script 1) surpasses the argumentation script (script 2) with regard to the overall depth of critical thinking in the discussion, and the critical thinking processes during the stages of problem identification and problem integration in particular. Further, it can be argued that students in the labeling condition are engaged in more focused, more critical, and more practically-oriented discussions.

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Pal, U., and B. B. Chaudhuri. "Identification of different script lines from multi-script documents." Image and Vision Computing 20, no. 13-14 (December 2002): 945–54. http://dx.doi.org/10.1016/s0262-8856(02)00101-4.

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Obaidullah, Sk Md, Chayan Halder, Nibaran Das, and Kaushik Roy. "Bangla and Oriya Script Lines Identification from Handwritten Document Images in Tri-script Scenario." International Journal of Service Science, Management, Engineering, and Technology 7, no. 1 (January 2016): 43–60. http://dx.doi.org/10.4018/ijssmet.2016010103.

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In this paper, two popular eastern Indian scripts namely Bangla and Oriya are considered for Line-level script identification considering two Tri-script groups where Devnagari and Roman are kept common in each group. A 27 dimensional feature vector has been constructed using FD (Fractal Dimension) and IMT (Interpolated Morphological Transform). 600 Line-level handwritten document images of each Tri-script groups have been considered for experimentation. Promising results has been found using multiple classifiers where MLP (Multi-Layer Perceptron) Neural Network and LMT (Logistic Model Tree) perform best for BDR (Bangla-Devnagari-Roman) combinations with 97% accuracy and LMT outperforms over others for ODR (Oriya-Devnagari-Roman) combinations with 97.7% accuracy. Bi-script performance analysis has also been made where combinations BR (Bangla-Roman) and BD (Bangla-Devnagari) results with accuracy of 98% and 97.5% respectively for the first group. Whereas for the second group OD (Oriya-Devnagari) and OR (Oriya-Roman) shows an accuracy of 98.25% and 98% respectively.

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Kaur, Amanpreet, Mohinder Singh, and Om Prakash Jasuja. "Interscript comparison of handwriting features leading to their identification and authorship." Nowa Kodyfikacja Prawa Karnego 45 (December 29, 2017): 15–36. http://dx.doi.org/10.19195/2084-5065.45.3.

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Identification of handwriting found on the disputed document by comparison with the known handwriting samples of the suspect still comprise the problem which is most commonly referred to a forensic document examiner. One of the important scientifically established principles which govern such analysis and identification is the ‘Principle of Comparison’ which explicitly states that, for obtaining correct results, like has to be compared with like; meaning thereby that the expert has to analyze and rely upon similar letters and combinations between the questioned and the standard handwriting samples and, consequently, the problems where similar handwriting samples in the same script have not been provided for comparison; usually fall outside the scope of forensic document examination. However, in this field, like any other human activity; perfect and ideal conditions are hard to achieve. Handwriting, being acquired skill and neuro-muscular controlled motor activity, its basic elements like the horizontal stroke, vertical stroke, loops, curves and arches etc., are combined together to form letters and alphabets of all the scripts. The question then arises — whether inter-script comparison of handwriting samples can be attempted leading to some limited or qualified conclusions. Thus, if it becomes possible and practicable to examine and compare the basic elements of questioned handwriting in one script, say Devanagri with the similar elements found in specimen/ admitted handwriting samples in another script by the same writer, say Gurmukhi, because sample handwritings in Devanagri could not be procured for whatsoever reasons; the scope of examination can be widened further and expert may be in a position to express some opinion regarding their common authorship or otherwise, which may be found worthwhile to the investigating agency or the court of law, thereby helping in the administration of justice ultimately.To the best of our knowledge, not much research is available, where writings produced in different scripts by the same writer could be compared, thereby leading to a definite opinion on the issue of their common authorship or otherwise. In the present study, an attempt has been made to explore this issue by taking writing samples of the same writer in three scripts, having knowledge of all the three commonly used languages, i.e., English, Hindi, and Punjabi, corresponding to the said scripts i.e., Roman, Devanagari and Gurumukhi. Three hundred sixty 360 writing samples were obtained from as many as 40 individuals appropriately skilled in writing, reading and speaking these languages/ scripts. Careful study and evaluation of the basic elements of written strokes whose execution were found to be similar in all the three scripts has been carried out indicating the possibility of ‘Script Independent Comparison’. Limitations of the proposed study have also been discussed in the paper.

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Yasir, Muhammad, Li Chen, Amna Khatoon, Muhammad Amir Malik, and Fazeel Abid. "Mixed Script Identification Using Automated DNN Hyperparameter Optimization." Computational Intelligence and Neuroscience 2021 (December 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/8415333.

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Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This study tackles the challenge of mixed script identification for mixed-code dataset consisting of Roman Urdu, Hindi, Saraiki, Bengali, and English. The language identification model is trained using word vectorization and RNN variants. Moreover, through experimental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Experimentation achieved the highest accuracy of 90.17 for Bi-GRU, applying learned word class features along with embedding with GloVe. Moreover, this study addresses the issues related to multilingual environments, such as Roman words merged with English characters, generative spellings, and phonetic typing.

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Pati, Peeta Basa, and A. G. Ramakrishnan. "Word level multi-script identification." Pattern Recognition Letters 29, no. 9 (July 2008): 1218–29. http://dx.doi.org/10.1016/j.patrec.2008.01.027.

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Sahare, Parul, and Sanjay B. Dhok. "Script identification algorithms: a survey." International Journal of Multimedia Information Retrieval 6, no. 3 (July 29, 2017): 211–32. http://dx.doi.org/10.1007/s13735-017-0130-2.

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16

Carroll, Marie, and Peter Freebody. "Script-based cues in identification." Acta Psychologica 64, no. 2 (February 1987): 105–21. http://dx.doi.org/10.1016/0001-6918(87)90001-1.

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Dhandra, B. V., Satishkumar Mallappa, and Gururaj Mukarambi. "Script Identification of Camera Based Bilingual Document Images Using SFTA Features." International Journal of Technology and Human Interaction 15, no. 4 (October 2019): 1–12. http://dx.doi.org/10.4018/ijthi.2019100101.

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In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

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Obaidullah, Sk Md, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Handwritten Indic Script Identification in Multi-Script Document Images: A Survey." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 10 (June 20, 2018): 1856012. http://dx.doi.org/10.1142/s0218001418560128.

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Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challenges.

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Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (April 2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.

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Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).

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Joshi, Gopal Datt, Saurabh Garg, and Jayanthi Sivaswamy. "A generalised framework for script identification." International Journal of Document Analysis and Recognition (IJDAR) 10, no. 2 (April 3, 2007): 55–68. http://dx.doi.org/10.1007/s10032-007-0043-3.

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Dhanya, D., A. G. Ramakrishnan, and Peeta Basa Pati. "Script identification in printed bilingual documents." Sadhana 27, no. 1 (February 2002): 73–82. http://dx.doi.org/10.1007/bf02703313.

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Arfiani, Ika, Murien Nugraheni, and Danang Sulistyono. "Implementasi SCRUM Pada Pengenalan Aksara Lampung Menggunakan Augmented Reality." Building of Informatics, Technology and Science (BITS) 3, no. 3 (December 31, 2021): 353–60. http://dx.doi.org/10.47065/bits.v3i3.1011.

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Lampung Province has 20 Lampung characters and 12 Lampung scripts as characters that need to be preserved. Lampung script learning is currently still using conventional methods so that many students begin to ignore this subject because it feels less interesting and boring. This study aims to build an application that can help users introduce Lampung script in the world of education on smartphones by applying Augmented Reality technology. By applying Marker Based Tracking which is one of the methods used in the development of Augmented Reality technology. This method works by recognizing and identifying patterns on markers to bring up virtual objects into the real environment. The system development uses the waterfall method with the stages of problem identification, initial planning, design and design, implementation, testing, and evaluation. This results in an Augmented Reality application to introduce Lampung script which is equipped with features showing 3D Lampung script, pronunciation of each Lampung script object, script gallery, guide for each menu and 20 Lampung script markers. Which type of smartphone camera can affect the application's ability to see objects at a certain angle to the marker, but is still quite safe at angles between 500 to 1800.

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Singh, Pawan Kumar, Ram Sarkar, Nibaran Das, Subhadip Basu, and Mita Nasipuri. "Statistical comparison of classifiers for script identification from multi-script handwritten documents." International Journal of Applied Pattern Recognition 1, no. 2 (2014): 152. http://dx.doi.org/10.1504/ijapr.2014.063741.

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Faqih, Fiyan Ilman. "INOVASI PEMBELAJARAN PENULISAN NASKAH DRAMA ANAK DENGAN MENGGUNAKAN STRATEGI IDCD (IDENTIFICATION, DESIGN, CHANGE, DAN DEVELOPMENT)." Jurnal Pendidikan Bahasa dan Sastra Indonesia Metalingua 5, no. 2 (November 13, 2020): 87–94. http://dx.doi.org/10.21107/metalingua.v5i2.8644.

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The writing children's drama scripts is an underdeveloped skill. This happens because students not understand who children and how characteristic children. One way to solve this problem is to innovate learning strategies. The strategic innovation is IDCD (identification, design, change, and development). This strategy was created for the needs of writing skills, especially writing children's drama scripts for students. Strategies were created to create learning more creative, innovative children's drama script writing, and children's drama scripts that are created according to the child's level of development.

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Unseth, Peter. "Sociolinguistic parallels between choosing scripts and languages." Written Language and Literacy 8, no. 1 (October 26, 2005): 19–42. http://dx.doi.org/10.1075/wll.8.1.02uns.

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This article demonstrates that many of the same concepts and tools developed for the sociolinguistic study of how language communities choose spoken languages can also be profitably applied to the study of how they choose scripts. These similarities include the choice of a language or script to either identify with or create distance from another group, borrowing elements from other languages and scripts, the death of languages and scripts, contact induced change in languages and scripts, and the identification of languages and scripts with gender.

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Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Offline Script Identification from multilingual Indic-script documents: A state-of-the-art." Computer Science Review 15-16 (February 2015): 1–28. http://dx.doi.org/10.1016/j.cosrev.2014.12.001.

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Hangarge, Mallikarjun, and B. V. Dhandra. "Offline Handwritten Script Identification in Document Images." International Journal of Computer Applications 4, no. 6 (July 10, 2010): 6–10. http://dx.doi.org/10.5120/834-1170.

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Chanda, Sukalpa, Umapada Pal, and Oriol Ramos Terrades. "Word-Wise Thai and Roman Script Identification." ACM Transactions on Asian Language Information Processing 8, no. 3 (August 2009): 1–21. http://dx.doi.org/10.1145/1568292.1568294.

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Obaidullah, Sk Md, Chayan Halder, Nibaran Das, and Kaushik Roy. "Numeral Script Identification from Handwritten Document Images." Procedia Computer Science 54 (2015): 585–94. http://dx.doi.org/10.1016/j.procs.2015.06.067.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Feature Selection Using Harmony Search for Script Identification from Handwritten Document Images." Journal of Intelligent Systems 27, no. 3 (July 26, 2018): 465–88. http://dx.doi.org/10.1515/jisys-2016-0070.

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Abstract The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the irrelevant, noisy, and non-contributing features, resulting in acceptable classification accuracy. Harmony search algorithm (HSA) is an evolutionary algorithm that is applied to various optimization problems such as scheduling, text summarization, water distribution networks, vehicle routing, etc. This paper presents a hybrid approach based on support vector machine and HSA for wrapper feature subset selection. This approach is used to select an optimized set of features from an initial set of features obtained by applying Modified log-Gabor filters on prepartitioned rectangular blocks of handwritten document images written in either of 12 official Indic scripts. The assessment justifies the need of feature selection for handwritten script identification where local and global features are computed without knowing the exact importance of features. The proposed approach is also compared with four well-known evolutionary algorithms, namely genetic algorithm, particle swarm optimization, tabu search, ant colony optimization, and two statistical feature dimensionality reduction techniques, namely greedy attribute search and principal component analysis. The acquired results show that the optimal set of features selected using HSA gives better accuracy in handwritten script recognition.

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Sharma, Shubhankar, and Vatsala Arora. "Script Identification for Devanagari and Gurumukhi using OCR." International Journal of Computer Science and Mobile Computing 10, no. 9 (September 30, 2021): 12–22. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.002.

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The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

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KumarShukla, Manoj, and Haider Banka. "Degraded Script Identification for Indian Language- A Survey." International Journal of Computer Applications 108, no. 6 (December 18, 2014): 11–22. http://dx.doi.org/10.5120/18914-0222.

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Chaudhari, Shailesh, and Ravi M. Gulati. "Script Identification Using Gabor Feature and SVM Classifier." Procedia Computer Science 79 (2016): 85–92. http://dx.doi.org/10.1016/j.procs.2016.03.012.

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Hochberg, Judith, Kevin Bowers, Patrick Kelly, and Michael Cannon. "Script and language identification for handwritten document images." International Journal on Document Analysis and Recognition 2, no. 2-3 (December 1, 1999): 45–52. http://dx.doi.org/10.1007/s100320050036.

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Vikram, T. N., and K. Chidananda Gowda. "Subspace models for document script and language identification." International Journal of Imaging Systems and Technology 20, no. 2 (May 19, 2010): 140–48. http://dx.doi.org/10.1002/ima.20215.

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Sudarma, Made, and I. Wayan Agus Surya Darma. "The Identification of Balinese Scripts’ Characters in Papyrus Based on Semantic Feature and K Nearest Neighbor." International Journal of Software Engineering and Technologies (IJSET) 1, no. 3 (December 1, 2016): 177. http://dx.doi.org/10.11591/ijset.v1i3.4580.

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Papyrus script is a cultural heritage in Bali. As we know, that the papyrus is a cultural matter which is rich in valuable cultural values. Issues or problems encountered today is that the papyrus are not well maintained. Thus, many papyrus becomes damaged because it is not stored properly. Papyrus script was written using Balinese script’s characters which having different features compared with Latin’s characters. Balinese script can be recognized with feature extraction owned by each Balinese script. KNN is a classification algorithm based on nearest neighborhood. KNN can be used to classify Balinese script’s features so that the test Balinese script’s features which having nearest neighborhood value with the trained Balinese script’s features will be recognized as the same Balinese script

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Obaidullah, Sk Md, Chayan Halder, K. C. Santosh, Nibaran Das, and Kaushik Roy. "PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification." Multimedia Tools and Applications 77, no. 2 (January 18, 2017): 1643–78. http://dx.doi.org/10.1007/s11042-017-4373-y.

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Putri, Tesa Ananda, Tri Suratno, and Ulfa Khaira. "Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 16, no. 2 (April 30, 2022): 205. http://dx.doi.org/10.22146/ijccs.70939.

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Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in society. This study aims to identify the characters of the Incung (Kerinci) script with the output in the form of Latin characters from the Incung script. The classification method used is the Convolutional Neural Network (CNN) method. The dataset used as many as 1400 incung character images divided into 28 classes. In this study, an experiment was conducted to obtain the most optimal model. Showing the results using the CNN method during the training process that the accuracy of the training data reaches 99% and the accuracy of the testing data reaches 91% by using the optimal hyperparameters from the tests that have been done, namely batch size 32, epoch 100, and Adam's optimizer. It evaluates the CNN model using 80 images in words (a combination of several characters) with 4 test scenarios. It shows that the model can recognize image data from scanning printed books, digital writing test data, test data with images containing more than two characters, and check images with different font sizes

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Ghosh, Rajib, Partha Pratim Roy, and Prabhat Kumar. "Smart Device Authentication Based on Online Handwritten Script Identification and Word Recognition in Indic Scripts Using Zone-Wise Features." International Journal of Information System Modeling and Design 9, no. 1 (January 2018): 21–55. http://dx.doi.org/10.4018/ijismd.2018010102.

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Secure authentication is a vital component for device security. The most basic form of authentication is by using passwords. With the evolution of smart devices, selecting stronger and unbreakable passwords have become a challenging task. Such passwords if written in native languages tend to offer improved security since attackers having no knowledge of such scripts finding it hard to crack. This article proposes two zone-wise feature extraction approaches - zone-wise structural and directional (ZSD) and zone-wise slopes of dominant points (ZSDP), to recognize online handwritten script and word in four major Indic scripts - Devanagari, Bengali, Telugu and Tamil. These features have been used separately and in combination in HMM-based platform for recognition purpose. The dimension reduction of the ZSD-ZSDP combination with factor analysis has shown the best performance in all the four scripts. This work can be utilized for setting up the authentication schemes with the Indic scripts' passwords thus rendering it difficult to crack by hackers having no knowledge of such scripts.

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Rani, Rajneesh, Renu Dhir, and Gurpreet Singh Lehal. "Modified Gabor Feature Extraction Method for Word Level Script Identification- Experimentation with Gurumukhi and English Scripts." International Journal of Signal Processing, Image Processing and Pattern Recognition 6, no. 5 (October 31, 2013): 25–38. http://dx.doi.org/10.14257/ijsip.2013.6.5.03.

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Berger, Gail Ann, and Caroline McGrath. "Sticking to the Script: Employee Identification and Brand Representation." Management Education: An International Journal 14, no. 3-4 (2015): 1–16. http://dx.doi.org/10.18848/2327-8005/cgp/v14i3-4/50881.

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Echi, Afef Kacem. "Automatic Script and Type Identification in Bi-lingual Forms." International Journal of Computing and Information Sciences 12, no. 1 (September 29, 2016): 25–32. http://dx.doi.org/10.21700/ijcis.2016.104.

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Biradar, Smita, Malemath V.S., and Suneel C. Shinde. "Word-wise Script Identification of South Indian Document Images." IJARCCE 4, no. 8 (August 30, 2015): 478–81. http://dx.doi.org/10.17148/ijarcce.2015.48103.

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Ahmed, Ahmed Abdullah, Harith Raad Hasan, Fariaa Abdalmajeed Hameed, and Omar Ismael Al-Sanjary. "Writer Identification on Multi-Script Handwritten Using Optimum Features." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 178–85. http://dx.doi.org/10.24017/science.2017.3.64.

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Recognizing the writer of a text that has been handwritten is a very intriguing research problem in the field of document analysis and recognition. This study tables an automatic way of recognizing the writer from handwritten samples. Even though much has been done in previous researches that have presented other various methods, it is still clear that the field has a room for improvement. This particular method uses Optimum Features based writer characterization. Here, each of the samples written is grouped according to their set of features that are acquired from a computed codebook. This proposed codebook is different from the others which segment the samples into graphemes by fragmenting a certain part of the writing known as ending strokes. The proposed technique is employed to a locate and extract the handwriting fragments from ending zone and then grouped the similar fragments to generate a new cluster known as ending cluster. The cluster that comes in handy in the process of coming up with the ending codebook through picking out the center of the same fragment group. The process is finalized by evaluating the proposed method on four datasets of the various languages. This method being proposed had an impressive 97.12% identification rate which is rates the best result on the ICFHR dataset.

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Bashir, Rumaan, and S. M. K. Quadri. "Density Based Script Identification of a Multilingual Document Image." International Journal of Image, Graphics and Signal Processing 7, no. 2 (January 8, 2015): 8–14. http://dx.doi.org/10.5815/ijigsp.2015.02.02.

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Obaidullah, Sk Md, Supratik Kundu Das, and Kaushik Roy. "A System for Handwritten Script Identification From Indian Document." Journal of Pattern Recognition Research 8, no. 1 (2013): 1–12. http://dx.doi.org/10.13176/11.485.

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Selamat, Ali, and Choon‐Ching Ng. "Arabic script language identification using letter frequency neural networks." International Journal of Web Information Systems 4, no. 4 (November 21, 2008): 484–500. http://dx.doi.org/10.1108/17440080810919503.

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Sivakumar, P., and Kilvisharam Oziuddeen Mohammed Aarif. "Cursive script identification using Gabor features and SVM classifier." International Journal of Computer Aided Engineering and Technology 12, no. 3 (2020): 328. http://dx.doi.org/10.1504/ijcaet.2020.10027369.

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Aarif, Kilvisharam Oziuddeen Mohammed, and P. Sivakumar. "Cursive script identification using Gabor features and SVM classifier." International Journal of Computer Aided Engineering and Technology 12, no. 3 (2020): 328. http://dx.doi.org/10.1504/ijcaet.2020.106230.

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Abuhaiba, Ibrahim S. I. "Discrete Script or Cursive Language Identification from Document Images." Journal of King Saud University - Engineering Sciences 16, no. 2 (2004): 253–68. http://dx.doi.org/10.1016/s1018-3639(18)30790-6.

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