Journal articles: 'Script Recognition'

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Rao, P. V. S. "Script recognition." Sadhana 19, no. 2 (April 1994): 257–70. http://dx.doi.org/10.1007/bf02811898.

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Sharma, Abhinav, Dhiren Soneji, Aabha Ranade, Dhwani Serai, Priya RL, CS Lifna, and Shashikant R. Dugad. "Gujarati Script Recognition." Procedia Computer Science 218 (2023): 2287–98. http://dx.doi.org/10.1016/j.procs.2023.01.204.

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Liu, Zhiji. "Optical character recognition and the smart ancient script database." Journal of Chinese Writing Systems 4, no. 4 (November 25, 2020): 255–69. http://dx.doi.org/10.1177/2513850220967758.

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The initial success of optical character recognition (OCR) for ancient scripts has opened the floodgates for ‘smart’ ancient script research. ‘Smart’ ancient script research requires the support of a smart ancient script database. In order to compile the big data necessary for this smart research, smart ancient script database software must be able to recognize all aspects and all levels of all ancient script materials. Therefore, in addition to the integration of OCR functionality into this software, the other primary imperative moving forward is to innovate a new digitized ancient script data system, one that includes full-scale supplementation to include all available materials, as well as newly inputted image data. This data must include variant graphic forms, variant written forms, handwriting, graphic components, calligraphic styles, and other of the inexhaustible different variations in script construction. This database must contain a multi-level framework with an annotated arrangement of the fullest range of meanings for words within linguistic context. It must also contain a digitally integrated multiple-path indexed arrangement of the important paleographical interpretations in the field. Our strategy for the construction of this smart ancient script database is to push forward with both algorithm writing and data input work simultaneously and in mutual support, following an open-sourced community supported model, making this project an exercise in interdisciplinary collaboration within the paleography community.

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Pino, Rodney, Renier Mendoza, and Rachelle Sambayan. "A Baybayin word recognition system." PeerJ Computer Science 7 (June 16, 2021): e596. http://dx.doi.org/10.7717/peerj-cs.596.

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Baybayin is a pre-Hispanic Philippine writing system used in Luzon island. With the effort in reintroducing the script, in 2018, the Committee on Basic Education and Culture of the Philippine Congress approved House Bill 1022 or the ”National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Since then, Baybayin OCR has become a field of research interest. Numerous works have proposed different techniques in recognizing Baybayin scripts. However, all those studies anchored on the classification and recognition at the character level. In this work, we propose an algorithm that provides the Latin transliteration of a Baybayin word in an image. The proposed system relies on a Baybayin character classifier generated using the Support Vector Machine (SVM). The method involves isolation of each Baybayin character, then classifying each character according to its equivalent syllable in Latin script, and finally concatenate each result to form the transliterated word. The system was tested using a novel dataset of Baybayin word images and achieved a competitive 97.9% recognition accuracy. Based on our review of the literature, this is the first work that recognizes Baybayin scripts at the word level. The proposed system can be used in automated transliterations of Baybayin texts transcribed in old books, tattoos, signage, graphic designs, and documents, among others.

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Sutramiani, Ni Putu, I. Wayan Agus Surya Darma, and Dewa Made Sri Arsa. "Handwritten Balinese Script Recognition on Palm Leaf Manuscript using Projection Profile and K-Nearest Neighbor." Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) 10, no. 3 (December 28, 2022): 133. http://dx.doi.org/10.24843/jim.2022.v10.i03.p02.

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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.

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Muhdalifah, Mujastia Feliati. "Pooling Comparison in CNN Architecture for Javanese Script Classification." International Journal of Informatics and Computation 3, no. 2 (January 10, 2022): 15. http://dx.doi.org/10.35842/ijicom.v3i2.30.

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Javanese script is evidence of the past culture, which contains various current language learning, including script recognition. However, learning traditional scripts becomes less attractive to the students. Thus, we propose a learning method to enable character recognition among students to deal with the issues. We offer a novel CNN architecture and compare different pooling layers for Javanese script classification. We calculate the separate pooling layer to reduce extensive feature extraction of the image. We present the model comparison results in Javanese character classification to convince our development.

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Yaseen, Rasty, and Hossein Hassani. "Kurdish Optical Character Recognition." UKH Journal of Science and Engineering 2, no. 1 (June 30, 2018): 18–27. http://dx.doi.org/10.25079/ukhjse.v2n1y2018.pp18-27.

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Currently, no offline tool is available for Optical Character Recognition (OCR) in Kurdish. Kurdish is spoken in different dialects and uses several scripts for writing. The Persian/Arabic script is widely used among these dialects. The Persian/Arabic script is written from Right to Left (RTL), it is cursive, and it uses unique diacritics. These features, particularly the last two, affect the segmentation stage in developing a Kurdish OCR. In this article, we introduce an enhanced character segmentation based method which addresses the mentioned characteristics. We applied the method to text-only images and tested the Kurdish OCR using documents of different fonts, font sizes, and image resolutions. The results of the experiments showed that the accuracy rate of character recognition of the proposed method was 90.82% on average.

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Singh, Ajay Pratap, and Ashwin Kumar Kushwaha. "Analysis of Segmentation Methods for Brahmi Script." DESIDOC Journal of Library & Information Technology 39, no. 2 (March 11, 2019): 109–16. http://dx.doi.org/10.14429/djlit.39.2.13615.

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Segmentation is an important step for developing any optical character recognition (OCR) system, which has to be redesigned for each script having, non-uniform nature/property. It is used to decompose the image into its sub-units, which act as a basis for character recognition. Brahmi is a non-cursive ancient script, in which characters are not attached to each other and have some spacing between them. This study analyses various segmentation methods for different scripts to develop the best suitable segmentation method for Brahmi. MATLAB software was used for segmentation purpose in the experiment. The sample data belongs to Brahmi script-based ‘Rumandei inscription’. In this paper, we discuss a segmentation methodology for distinct components, namely text lines, words and characters of Rumandei inscription, written in Brahmi script. For segmenting distinct components of inscription different approach were used like horizontal projection profile, vertical projection profile and Relative minima approach. This is fundamental research on an inscription based on Brahmi script, which acts as a foundation for developing a segmentation module of an OCR solution/system of similar scripts in future. Information search and retrieval is an important activity of a library. So, to ensure this support for digitised documents written in ancient script, their character recognition is mandatory through the OCR system.

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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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Namboodiri, A. M., and A. K. Jain. "Online handwritten script recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 1 (January 2004): 124–30. http://dx.doi.org/10.1109/tpami.2004.1261096.

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Ghosh, D., T. Dube, and A. P. Shivaprasad. "Script Recognition—A Review." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 12 (December 2010): 2142–61. http://dx.doi.org/10.1109/tpami.2010.30.

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Gordin, Shai. "Optical character recognition for ancient non-alphabetic scripts." Open Access Government 36, no. 1 (October 4, 2022): 280–81. http://dx.doi.org/10.56367/oag-036-10262.

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Optical character recognition for ancient non-alphabetic scripts Cuneiform is one of the earliest writing systems in the world, invented at the end of the fourth millennium BCE. It is usually written by pressing a stylus on moist clay tablets, creating a three-dimensional script. The script is logo-syllabic, like the Chinese or Japanese writing systems, meaning the same sign can be read logographically, as a word, as syllables, or as determinatives (ie semantic classifiers). The correct reading depends on the context. There are close to a thousand cuneiform signs, not all of which were used simultaneously; usually about 200-300 signs were used at once. This article shows Shai Gordin, Senior Lecturer at Digital Pasts Lab in Ariel University, look at the deciphering of ancient non-alphabetic scripts, and the technology we use to understand it.

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Varpe, Kanchan, and Sachin Sakhare. "Review of Character Recognition Techniques for MODI Script." Indian Journal Of Science And Technology 16, no. 26 (July 23, 2023): 1935–46. http://dx.doi.org/10.17485/ijst/v16i26.485.

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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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VAJDA, S., K. ROY, U. PAL, B. B. CHAUDHURI, and A. BELAID. "AUTOMATION OF INDIAN POSTAL DOCUMENTS WRITTEN IN BANGLA AND ENGLISH." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 08 (December 2009): 1599–632. http://dx.doi.org/10.1142/s0218001409007776.

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In this paper, we present a system towards Indian postal automation based on pin-code and city name recognition. Here, at first, using Run Length Smoothing Approach (RLSA), non-text blocks (postal stamp, postal seal, etc.) are detected and using positional information, Destination Address Block (DAB) is identified from postal documents. Next, lines and words of the DAB are segmented. In India, the address part of a postal document may be written by a combination of two scripts: Latin (English) and a local (State/region) script. It is very difficult to identify the script by which pin-code part is written. To overcome this problem on pin-code part, we have used a two-stage artificial neural network based general scheme to recognize pin-code numbers written in any of the two scripts. To identify the script by which a word/city name is written, we propose a water reservoir concept based feature. For recognition of city names, we propose an NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the proposed digit numeral recognition module is 93.14% while that of city name recognition scheme is 86.44%.

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Pino, Rodney, Renier Mendoza, and Rachelle Sambayan. "Optical character recognition system for Baybayin scripts using support vector machine." PeerJ Computer Science 7 (February 15, 2021): e360. http://dx.doi.org/10.7717/peerj-cs.360.

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In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.

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Agnihotri, Ved Prakash. "Offline Handwritten Devanagari Script Recognition." International Journal of Information Technology and Computer Science 4, no. 8 (July 16, 2012): 37–42. http://dx.doi.org/10.5815/ijitcs.2012.08.04.

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MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (August 2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

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Pohl, R�diger, Hans Colonius, and Manfred Th�ring. "Recognition of script-based inferences." Psychological Research 47, no. 1 (April 1985): 59–67. http://dx.doi.org/10.1007/bf00309219.

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Goss, Seth. "Word recognition in a language with multiple orthographies: A semantic masked-priming study of L1 Mandarin learners of L3 Japanese." Journal of Japanese Linguistics 35, no. 2 (November 26, 2019): 235–55. http://dx.doi.org/10.1515/jjl-2019-2012.

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Abstract This study explored the organization of the multilingual lexicon in L3 learners of Japanese from an L1 Mandarin Chinese background. Using a masked-priming paradigm, it examined whether native-language translations of Japanese words facilitated the recognition of native-morpheme hiragana words and katakana-script loanwords to a similar degree. Participants performed a lexical decision task on a series of hiragana and katakana words, which were preceded by three prime types: noncognate translations, same-script duplicates, and unrelated words. Results showed an equal magnitude of priming from L1 translations for L3 Japanese targets in both scripts, suggesting that conceptual information is made rapidly available for word recognition via an L1 prime. However, priming in the same-script duplicate condition differed numerically between hiragana and katakana, indicating that lower-familiarity katakana loanwords are not activated as rapidly as words in the more-familiar hiragana script. Findings are discussed in relation to models of the multilingual lexicon.

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Wiguna, I. Komang Arya Ganda, and I. Made Dwi Putra Asana. "IMPLEMENTASI ZONING DAN FITUR ARAH SEBAGAI EKSTRAKSI FITUR PADA PENGENALAN TULISAN TANGAN AKSARA BALI." Jurnal RESISTOR (Rekayasa Sistem Komputer) 4, no. 1 (April 21, 2021): 85–92. http://dx.doi.org/10.31598/jurnalresistor.v4i1.751.

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Character recognition is one of the most researched fields in computer science. Combining the field of digital image processing and pattern recognition is a challenge in determining the most optimal method combination to complete character recognition. Balinese script is one of the regional scripts used in Balinese literary. The challenge with Balinese script is that some of its characters have a degree of similarity. So far, several methods of feature extraction that have been studied for Balinese script are modified direction feature, template matching, image centroid zone and zone centroid zone, local binary pattern. In this research, we combine methods based on zoning and directional features. The methods used are ICZ, ZCZ and freeman chain code to find the characteristics of Balinese script handwriting. The addition of chain code method aims to determine the value around the foreground point. The results of feature extraction will be used as input in the Support Vector Machine for the classification process. The test result shows that the combination of the ICZ, ZCZ and freeman chain code methods produces an accuracy of 89.09%, while the combination of ICZ and ZCZ produces 88.06% of accuracy. The SVM kernels compared use linear kernels.

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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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Komatsu, Shin-Ichi, Masaru Mimura, Motoichiro Kato, and Haruo Kashima. "Cross-Script and Within-Script Priming in Alcoholic Korsakoff Patients." Perceptual and Motor Skills 96, no. 2 (April 2003): 495–509. http://dx.doi.org/10.2466/pms.2003.96.2.495.

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In two experiments, alcoholic Korsakoff patients and control subjects studied a list of Japanese nouns written in either Hiragana or Kanji script. Word-fragment completion and recognition tests were then administered in Hiragana. When the writing script was changed between study and test phases, repetition priming in word-fragment completion was significantly attenuated but was still reliable against baseline performance. This was confirmed for both Korsakoff patients and control subjects. In contrast, the script change had little effect on recognition memory, which was severely impaired in Korsakoff patients. The results suggest that repetition priming is mediated by two different implicit processes, one that is script-specific and the other that is assumed to operate at a more abstract level.

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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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Pawar, Vijaya Rahul, and Arun Gaikwad. "Multistage Recognition Approach for Offline Handwritten Marathi Script Recognition." International Journal of Signal Processing, Image Processing and Pattern Recognition 7, no. 1 (February 28, 2014): 365–78. http://dx.doi.org/10.14257/ijsip.2014.7.1.34.

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KOJIMA, Masami, Yoshiyuki KAWAZOE, and Masayuki KIMURA. "Automatic Character Recognition for Tibetan Script." JOURNAL OF INDIAN AND BUDDHIST STUDIES (INDOGAKU BUKKYOGAKU KENKYU) 39, no. 2 (1991): 848–44. http://dx.doi.org/10.4259/ibk.39.848.

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Malanker, Aradhana A., and Prof Mitul M Patel. "Handwritten Devanagari Script Recognition: A Survey." IOSR Journal of Electrical and Electronics Engineering 9, no. 2 (2014): 80–87. http://dx.doi.org/10.9790/1676-09228087.

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Chhabra, Indu, and Chandan Singh. "Script Recognition: A Convoluted Neural Approach." International Journal of Technology, Knowledge, and Society 2, no. 8 (2007): 89–94. http://dx.doi.org/10.18848/1832-3669/cgp/v02i08/55687.

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Abu-Ain, Waleed Abdel Karim, Siti Norul Huda Sheikh Abdullah, Khairuddin Omar, and Siti Zaharah Abd. Rahman. "Automatic Multi-lingual Script Recognition Application." GEMA Online® Journal of Language Studies 18, no. 3 (August 29, 2018): 203–21. http://dx.doi.org/10.17576/gema-2018-1803-12.

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PAL, U., and B. B. CHAUDHURI. "Computer recognition of printed Bangla script." International Journal of Systems Science 26, no. 11 (November 1995): 2107–23. http://dx.doi.org/10.1080/00207729508929157.

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Bozinovic, R. M., and S. N. Srihari. "Off-line cursive script word recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 1 (1989): 68–83. http://dx.doi.org/10.1109/34.23114.

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MUKKULAINEN, RISTO. "Script Recognition with Hierarchical Feature Maps." Connection Science 2, no. 1-2 (January 1990): 83–101. http://dx.doi.org/10.1080/09540099008915664.

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Pal, U., and B. B. Chaudhuri. "Indian script character recognition: a survey." Pattern Recognition 37, no. 9 (September 2004): 1887–99. http://dx.doi.org/10.1016/j.patcog.2004.02.003.

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Chaudhuri, B. B., U. Pal, and M. Mitra. "Automatic recognition of printed Oriya script." Sadhana 27, no. 1 (February 2002): 23–34. http://dx.doi.org/10.1007/bf02703310.

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Munivel, Monisha, and V. S. Felix Enigo. "Optical Character Recognition for Printed Tamizhi Documents using Deep Neural Networks." DESIDOC Journal of Library & Information Technology 42, no. 4 (July 19, 2022): 227–33. http://dx.doi.org/10.14429/djlit.42.4.17742.

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Tamizhi (Tamil-Brahmi) script is one of the oldest scripts in India from which most of the modern Indian scripts are evolved. The ancient historical documents are generally preserved as digitised texts using Optical Character Recognition (OCR) technique. But the development of OCR for Tamizhi documents is highly challenging as many characters have similar shapes and structures with very small variations. In specific, for Tamizhi script it is very difficult to build an OCR as many characters are combined characters. This can be a single character formed by a single vowel/consonant or compound characters formed by combining vowels and consonants. This paper deals with the development of Tamizhi OCR for printed Tamizhi documents which is anticipated to perform efficiently irrespective of poor quality, noises and various input formats of Tamizhi documents. This is a preliminary study towards developing an OCR for handwritten Tamizhi inscription images that recognises text captured from onsite inscriptions. The developed Tamizhi OCR for printed text can produce an accuracy of about 91.12 per cent.

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Nakamura, Kimihiro, Stanislas Dehaene, Antoinette Jobert, Denis Le Bihan, and Sid Kouider. "Subliminal Convergence of Kanji and Kana Words: Further Evidence for Functional Parcellation of the Posterior Temporal Cortex in Visual Word Perception." Journal of Cognitive Neuroscience 17, no. 6 (June 1, 2005): 954–68. http://dx.doi.org/10.1162/0898929054021166.

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Recent evidence has suggested that the human occipito-temporal region comprises several subregions, each sensitive to a distinct processing level of visual words. To further explore the functional architecture of visual word recognition, we employed a subliminal priming method with functional magnetic resonance imaging (fMRI) during semantic judgments of words presented in two different Japanese scripts, Kanji and Kana. Each target word was preceded by a subliminal presentation of either the same or a different word, and in the same or a different script. Behaviorally, word repetition produced significant priming regardless of whether the words were presented in the same or different script. At the neural level, this cross-script priming was associated with repetition suppression in the left inferior temporal cortex anterior and dorsal to the visual word form area hypothesized for alphabetical writing systems, suggesting that cross-script convergence occurred at a semantic level. fMRI also evidenced a shared visual occipito-temporal activation for words in the two scripts, with slightly more mesial and right-predominant activation for Kanji and with greater occipital activation for Kana. These results thus allow us to separate script-specific and script-independent regions in the posterior temporal lobe, while demonstrating that both can be activated subliminally.

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Ratcliffe, Robert R. "What do “phonemic” writing systems represent?" Written Language and Literacy 4, no. 1 (March 19, 2001): 1–14. http://dx.doi.org/10.1075/wll.4.1.02rat.

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The traditional classification of phonemic writing systems into three types — syllabaries, consonantal scripts, and alphabets — is based on a phonological theory which recognizes only the syllable and the segment as potential units of representation. It is argued here that an accurate typology of phonemic writing systems requires recognition of two further dimensions of phonological structure: phonological time, and the sonority hierarchy. The analysis focuses on two “typical” non-alphabetic systems — Japanese kana and the Arabic script, the former traditionally classed as a syllabary, the latter as a consonantal script. It is argued that the two scripts in fact share a common organizational principle, namely the iconic representation of phonological time.

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BIADSY, FADI, RAID SAABNI, and JIHAD EL-SANA. "SEGMENTATION-FREE ONLINE ARABIC HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 07 (November 2011): 1009–33. http://dx.doi.org/10.1142/s0218001411008956.

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Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

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Luterbach, Kenneth J., and Diane Rodriguez. "Practicing Pronunciation: Will Voice XML do for language learners what HTML did for collaborators?" EuroCALL Review 11 (March 15, 2007): 11. http://dx.doi.org/10.4995/eurocall.2007.16368.

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This paper considers the utility of the Voice Extensible Markup Language (Voice XML) for language learning. In particular, this article considers whether Voice XML might become as popular as HTML. First, this paper discusses the surprising popularity of HTML, which provides contextual information useful for considering the potential of Voice XML. Second, this article discusses two voice scripts in order to demonstrate Voice XML tags and features. The first example script concerns voice synthesis only whereas the second script utilizes both voice synthesis and voice recognition. In order to gain insight into the utility of Voice XML for instructional applications, the second voice script can be accessed by language learners in order to practice pronouncing words in English. Technically, each voice script is a text file containing Voice XML tags. Once the file containing a Voice XML script is stored on a web server and a telephone number linked to the file, a language learner can use a telephone to practice pronouncing words. Those implementation details are considered in the third section of this paper, which identifies one particular system that permits developers to test and deploy Voice XML scripts free of charge. Lastly, this article concludes with a discussion of issues concerning the utility of Voice XML relative to HTML.

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Erdfelder, Edgar, and Jürgen Bredenkamp. "Recognition of script-typical versus script-atypical information: Effects of cognitive elaboration." Memory & Cognition 26, no. 5 (September 1998): 922–38. http://dx.doi.org/10.3758/bf03201173.

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Mukhopadhyay, Anirban, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "Handwritten Indic Script Recognition Based on the Dempster–Shafer Theory of Evidence." Journal of Intelligent Systems 29, no. 1 (February 6, 2018): 264–82. http://dx.doi.org/10.1515/jisys-2017-0431.

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Abstract In a multilingual country like India, script recognition is an important pre-processing footstep necessary for feeding any document to an optical character recognition (OCR) engine, which is, in general, script specific. The present work evaluates the performance of an ensemble of two MLP (multi-layer perceptron) classifiers, each trained on different feature sets. Here, two complementary sets of features, namely, gray-level co-occurrence matrix (GLCM) and Gabor wavelets transform coefficients are extracted from each of the handwritten text-line and word images written in 12 official scripts used in Indian subcontinent, which are then fed into an individual classifier. In order to improve the overall recognition rate, a powerful combination approach based on the Dempster–Shafer (DS) theory is finally employed to fuse the decisions of two MLP classifiers. The performance of the combined decision is compared with those of the individual classifiers, and it is noted that a significant improvement in recognition accuracy (about 4% for text-line data and 6% for word level data) has been achieved by the proposed methodology.

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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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Padmaja, Kannuru. "Devanagari Handwritten Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 102–5. http://dx.doi.org/10.22214/ijraset.2022.39744.

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Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

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Chen, Tingzhu, Yaoyao Qian, Jingyu Pei, Shaoteng Wu, Jiang Wu, Lin Li, and Jung-yueh Tu. "A study on encoding-based oracle bone script recognition." Journal of Chinese Writing Systems 4, no. 4 (December 2020): 281–90. http://dx.doi.org/10.1177/2513850220952890.

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Oracle bone script recognition (OBSR) has been a fundamental problem in research on oracle bone scripts for decades. Despite being intensively studied, existing OBSR methods are still subject to limitations regarding recognition accuracy, speed and robustness. Furthermore, the dependency of these methods on expert knowledge hinders the adoption of OBSR systems by the general public and also discourages social outreach of research outputs. Addressing these issues, this study proposes an encoding-based OBSR system that applies image pre-processing techniques to encode oracle images into small matrices and recognize oracle characters in the encoding space. We tested our methods on a collection of oracle bones from the Yin Ruins in XiaoTun village, and achieved a high accuracy rate of 99% within a time range of milliseconds.

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Sugianela, Yuna, and Nanik Suciati. "Javanese Document Image Recognition Using Multiclass Support Vector Machine." CommIT (Communication and Information Technology) Journal 13, no. 1 (May 31, 2019): 25. http://dx.doi.org/10.21512/commit.v13i1.5330.

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Some ancient documents in Indonesia are written in the Javanese script. Those documents contain the knowledge of history and culture of Indonesia, especially about Java. However, only a few people understand the Javanese script. Thus, the automation system is needed to translate the document written in the Javanese script. In this study, the researchers use the classification method to recognize the Javanese script written in the document. The method used is the Multiclass Support Vector Machine (SVM) using One Against One (OAO) strategy. The researchers use seven variations of Javanese script from the different document for this study. There are 31 classes and 182 data for training and testing data. The result shows good performance in the evaluation. The recognition system successfully resolves the problem of color variation from the dataset. The accuracy of the study is 81.3%.

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Zhang, Zhiyun, Elham Eli, Hornisa Mamat, Alimjan Aysa, and Kurban Ubul. "EA-ConvNeXt: An Approach to Script Identification in Natural Scenes Based on Edge Flow and Coordinate Attention." Electronics 12, no. 13 (June 27, 2023): 2837. http://dx.doi.org/10.3390/electronics12132837.

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In multilingual scene text understanding, script identification is an important prerequisite step for text image recognition. Due to the complex background of text images in natural scenes, severe noise, and common symbols or similar layouts in different language families, the problem of script identification has not been solved. This paper proposes a new script identification method based on ConvNext improvement, namely EA-ConvNext. Firstly, the method of generating an edge flow map from the original image is proposed, which increases the number of scripts and reduces background noise. Then, based on the feature information extracted by the convolutional neural network ConvNeXt, a coordinate attention module is proposed to enhance the description of spatial position feature information in the vertical direction. The public dataset SIW-13 has been expanded, and the Uyghur script image dataset has been added, named SIW-14. The improved method achieved identification rates of 97.3%, 93.5%, and 92.4% on public script identification datasets CVSI-2015, MLe2e, and SIW-13, respectively, and 92.0% on the expanded dataset SIW-14, verifying the superiority of this method.

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Bintoro, Panji, and Agus Harjoko. "Lampung Script Recognition Using Convolutional Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 16, no. 1 (January 31, 2022): 23. http://dx.doi.org/10.22146/ijccs.70041.

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The Lampung script is often used in writing words in Lampung language. The Lampung language itself is used by native Lampung people and people who learn Lampung language. The Lampung script is difficult to learn because there are many combinations of parent characters and subletters. CNN is a method in the field of object recognition that has a specific layer, namely a convolution layer and a pooling layer that allows the feature learning process well. Handwriting recognition as in character recognition in MNIST, CNN produces better performance compared to other methods. From the advantages of CNN, the CNN method with DenseNet architecture was chosen as the best architecture to recognize each Lampung script. In this study, there are 2 main processes, namely preprocessing, and recognition. This study succeeded in applying the CNN method which can recognize Lampung script. The dataset is divided into 4 groups of characters that have different sounds. First, the parent character data get 98% accuracy. Second, the parent letter data with the above letters get 98% accuracy. Third, the parent character data with the sub-letters on the side get 98% accuracy. Fourth, the parent letter data with the lower letters get 97% accuracy.

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Indrawan, Gede, Ahmad Asroni, Luh Joni Erawati Dewi, I. Gede Aris Gunadi, and I. Ketut Paramarta. "Balinese Script Recognition Using Tesseract Mobile Framework." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 13, no. 3 (November 25, 2022): 160. http://dx.doi.org/10.24843/lkjiti.2022.v13.i03.p03.

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One of the main factors causing the decline in the use of Balinese Script is that Balinese people are less interested in reading Balinese Script because of their reluctance to learn Balinese Script, which is relatively complicated in the recognition process. The development of computer technology has now been used to help by performing character recognition or known as Optical Character Recognition (OCR). Developing the OCR application for Balinese Script is an effort to help preserve, from the technology side, as a means of education related to Balinese Script. In this study, that development was conducted by using a Tesseract OCR engine that consists of several stages, i.e., the first one is to prepare the dataset, the second one is to generate the dataset using the Web Scraping method, the third one is to train the OCR engine using the generated dataset, and finally, the fourth one is to implement the generated language model into a mobile-based application. The study results prove that the dataset generation process using the Web Scraping method can be a better choice when faced with a training dataset that requires a large dataset compared to several previous studies of non-Latin character recognition. In those studies, the jTessBox tools were used, which took time because they had to select per character for a dataset. The best result of the language model is a combination of character, word, sentence, and paragraph datasets (hierarchical combination of character, word, sentence, and paragraph datasets) with a coincidence rate of 66.67%. The more diverse and structured hierarchical datasets used, the higher the coincidence rate.

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JyotiKumar, Chandan, and Sanjib Kumar Kalita. "Recognition of Handwritten Numerals of Manipuri Script." International Journal of Computer Applications 84, no. 17 (December 18, 2013): 1–5. http://dx.doi.org/10.5120/14674-2835.

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Sawant, U. M., R. K. Parkar, S. L. Shitole, and S. P. Deore. "Devanagari Script Recognition using Capsule Neural Network." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 208–11. http://dx.doi.org/10.26438/ijcse/v7i1.208211.

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Journal articles on the topic 'Script Recognition'

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