Relevant bibliographies by topics / Character Error Rate (CER)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Character Error Rate (CER)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Character Error Rate (CER)"

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Tilkar, Swati. "Generating Meeting Transcription Using Natural Language Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51091.

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Natural Language Processing plays a pivotal role in automating the transcription of meetings. It enables machines to understand, interpret, and generate human language. In meeting transcription, NLP components such as Automatic Speech Recognition (ASR), speaker diarization, entity recognition, summarization, and sentiment analysis work together to produce accurate and readable transcripts. ASR converts spoken words into text, while NLP refines the raw output by correcting grammatical errors, identifying speakers, and structuring dialogue for readability and comprehension. Ethical consideration
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Chen, Zeyuan, Cheng Zhong, and Danyang Chen. "A syllable-character collaborative model for enhanced Pinyin and Chinese recognition." PLOS One 20, no. 7 (2025): e0325045. https://doi.org/10.1371/journal.pone.0325045.

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In Chinese speech recognition, end-to-end speech recognition models usually use Chinese characters as direct output and perform poorly compared with other language models. The main reason for this phenomenon is that the relationship between Chinese text and pronunciation is more complex. Inspired by the learning process of Chinese beginners, who first master initials, finals, and pinyin before learning characters, we propose the Syllable-Character Collaborative Model (SCCM), which incorporates these phonetic elements into the training process. Additionally, we design a Pinyin-Ensemble module t
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Karima, Nida Aulia, Ade Nurul Aisyah, Hercio Venceslau Silla, Lekso Budi Handoko, and Ramadhan Rakhmat Sani. "Kriptografi Teks Berbasis Algoritma Substitusi Vigenere Cipher 8 Bit." Jurnal Masyarakat Informatika 15, no. 1 (2024): 1–13. http://dx.doi.org/10.14710/jmasif.15.1.60836.

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Vigenere Cipher merupakan salah satu algoritma kriptografi klasik dalam dunia kriptografi. Penelitian ini berfokus pada penggunaan metode Vigenere Cipher dan implementasinya dalam mengamankan sebuah teks pesan berbentuk ASCII. Penelitian ini menggunakan empat metode pengujian yaitu, Avalanche Effect, Character Error Rate (CER), Bit Error Rate (BER), dan Entropi. Hasil pengujian mendapatkan bahwa nilai Avalanche Effect yang dihasilkan rata-rata berada pada angka 50% ke atas, artinya diperoleh nilai Avalanche Effect yang baik. Selain itu, CER dan BER yang dihasilkan bernilai 0, artinya tidak ter
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Maurya, Maruti, Mohd Zaheer, Nawab Mohammad, Sadaf siddiqui, Mohd Zeeshan Khan, and Mohd Ayan Akram. "Speech Recognition Technologies: Design, Challenges, and Real-World Applications." International Journal of Innovative Research in Computer Science and Technology 13, no. 3 (2025): 55–61. https://doi.org/10.55524/ijircst.2025.13.3.9.

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This paper presents an automated speech recognition (ASR) system that transcribes audio from YouTube videos into accurate text using OpenAI's Whisper model. Leveraging tools such as yt_dlp, FFmpeg, and PyTorch, the system creates a robust speech-to-text pipeline. On receiving a video URL, the system extracts and preprocesses audio, transcribes it using Whisper, and evaluates transcription quality through metrics like Word Error Rate (WER), Character Error Rate (CER), and Match Error Rate (MER). The pipeline supports offline use, making it suitable for accessible, cost-effective deployment in e
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Cheema, Musa Dildar Ahmed, Mohammad Daniyal Shaiq, Farhaan Mirza, Ali Kamal, and M. Asif Naeem. "Adapting multilingual vision language transformers for low-resource Urdu optical character recognition (OCR)." PeerJ Computer Science 10 (April 29, 2024): e1964. http://dx.doi.org/10.7717/peerj-cs.1964.

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In the realm of digitizing written content, the challenges posed by low-resource languages are noteworthy. These languages, often lacking in comprehensive linguistic resources, require specialized attention to develop robust systems for accurate optical character recognition (OCR). This article addresses the significance of focusing on such languages and introduces ViLanOCR, an innovative bilingual OCR system tailored for Urdu and English. Unlike existing systems, which struggle with the intricacies of low-resource languages, ViLanOCR leverages advanced multilingual transformer-based language
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Abdallah, Abdelrahman, Mohamed Hamada, and Daniyar Nurseitov. "Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text." Journal of Imaging 6, no. 12 (2020): 141. http://dx.doi.org/10.3390/jimaging6120141.

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This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first
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Wicaksono, Agung, and Eka Setia Nugraha. "Desain Modem Sistem Komunikasi Digital HF Berbasis Software Defined Radio." Edu Komputika Journal 8, no. 1 (2021): 21–30. http://dx.doi.org/10.15294/edukomputika.v8i1.47297.

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Sistem komunikasi High Frequency (HF) bekerja menggunakan gelombang radio pada frekuensi 3-30 MHz yang merambat dalam bentuk skywave dengan bantuan lapisan ionosfer. Sistem komunikasi HF saat ini masih terbatas pada pengiriman suara, diharapkan dapat mengirimkan pesan berupa teks dengan menerapkan sistem komunikasi digital. Penelitian ini melaporkan desain modem sistem komunikasi digital HF menggunakan perangkat Software Defined Radio (SDR) untuk implementasi yang mudah. Modulasi dan Demodulasi memiliki peranan penting dalam sistem komunikasi digital. Evaluasi sistem dilakukan dengan eksperime
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Drobac, Senka, and Krister Lindén. "Optical character recognition with neural networks and post-correction with finite state methods." International Journal on Document Analysis and Recognition (IJDAR) 23, no. 4 (2020): 279–95. http://dx.doi.org/10.1007/s10032-020-00359-9.

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Abstract The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy (https://github.com/tmbdev/ocropy) and Tesseract (https://github.com/tesseract-ocr/tesseract), but so far, none of the methods have managed to successfully train a mixed model that
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Darpito, Muhammad Noko, Kartika Firdausy, and Abdul Fadlil. "Perbandingan Unjuk Kerja Library Optical Character Recognition (OCR) dalam Pengenalan Teks pada Dokumen Digital." Jurnal Informatika Polinema 11, no. 3 (2025): 273–82. https://doi.org/10.33795/jip.v11i3.7025.

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Optical Character Recognition (OCR) merupakan teknologi yang digunakan untuk mengubah teks dalam dokumen digital menjadi teks yang dapat dikenali oleh mesin. Pemilihan metode OCR yang tepat sangat bergantung pada efisiensi pemrosesan dan akurasi pengenalan teks, terutama dalam penerapan yang membutuhkan kecepatan tinggi dan tingkat kesalahan minimal. Dalam penelitian ini, dilakukan perbandingan performa antara Tesseract dan EasyOCR melalui metode penelitian yang mencakup tahapan pengumpulan data, ekstraksi teks, implementasi OCR menggunakan kedua library tersebut, dan evaluasi hasil ekstraksi
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Tadesse, Direselign Addis, Chuan-Ming Liu, and Van-Dai Ta. "Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition." Information 14, no. 12 (2023): 654. http://dx.doi.org/10.3390/info14120654.

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Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing
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Dissertations / Theses on the topic "Character Error Rate (CER)"

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Laryea, Joycelyn, and Nipunika Jayasundara. "Automatic Speech Recognition System for Somali in the interest of reducing Maternal Morbidity and Mortality." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34436.

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Developing an Automatic Speech Recognition (ASR) system for the Somali language, though not novel, is not actively explored; hence there has been no success in a model for conversational speech. Neither are related works accessible as open-source. The unavailability of digital data is what labels Somali as a low resource language and poses the greatest impediment to the development of an ASR for Somali. The incentive to develop an ASR system for the Somali language is to contribute to reducing the Maternal Mortality Rate (MMR) in Somalia. Researchers acquire interview audio data regarding mate
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Book chapters on the topic "Character Error Rate (CER)"

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Strankale, Laine, and Pēteris Paikens. "OCR Challenges for a Latvian Pronunciation Dictionary." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200623.

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This paper covers the devlopment of a custom OCR solution based on the Tesseract open source engine developed for digitization of a Latvian pronunciation dictionary where the pronunciation data is described using a large variety of diacritic markings not supported by standard OCR solutions. We describe our efforts in training a model for these symbols without the additional support of preexisting dictionaries and illustrate how word error rate (WER) and character error rate (CER) are affected by changes in the dataset content and size. We also provide an error analysis and postulate possible c
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Sangeetha, J., D. Rekha, M. Priyanka, and M. Dhivya. "An Enhanced Real-Time Automatic Speech Recognition System for Tamil Language Using Wav2Vec2 Model." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1694-8.ch016.

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Automatic speech recognition (ASR) is a vital technology that transforms spoken language into written text, facilitating effective accessibility and communication. Despite the ongoing development of deep learning approaches, speech recognition remains a formidable task, especially for languages with limited data resources, such as Tamil. This work presents the development of an ASR system by utilizing the real-time spontaneous Tamil speech data collected from various types of people's communications in public places. The corpus is trained by fine-tuning the pre-trained wav2vec2 XLSR model. Thi
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Subramanian E.K. and Harsh. "Recognizing Handwritten Digits Using Multi-Dimensional Recurrent Neural Networks Intelligent Character Recognition (ICR) with Improved F-Score Measures." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220091.

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The goal of this study is to create a model that can recognize digits using Novel Recurrent Neural Networks (RNN) with LSTM cells and provide an F score comparison for optical character recognition versus Support Vector Machines (SVM) with Linear Kernel on the MNIST dataset. The sample estimation is done using the GPower statistical software with a pre-power test of 80%. The type-I error rate (alpha error rate) of 0.05 is considered. The dataset has 70K samples of handwritten digits, of which 60K are used as training samples and the remaining 10,000 are used as testing samples. In this researc
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"Front Matter." In An Introduction to the Development and Use of the Master Curve Method. ASTM International100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, 2005. http://dx.doi.org/10.1520/mnl10609m.

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This new ASTM manual introduces the concept of the Master Curve Method so it can be understood and used by engineers who have had limited exposure to elastic-plastic fracture mechanics and/or advanced statistical methods. It addresses the practical design-related problem of defining the ductile-to-brittle fracture transition temperature of structural steels directly in terms of fracture mechanics data. Topics cover: • Background and historical aspects • Data validity requirements imposed on test data, and the number of data required to constitute a statistically useable data set for determinin
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Conference papers on the topic "Character Error Rate (CER)"

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K, Thennal D., Jesin James, Deepa Padmini Gopinath, and Muhammed Ashraf K. "Advocating Character Error Rate for Multilingual ASR Evaluation." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.277.

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Park, Chanho, Hyunsik Kang, and Thomas Hain. "Character Error Rate Estimation for Automatic Speech Recognition of Short Utterances." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715433.

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Silva, Lucas Viana da, Paulo Lilles Jorge Drews Junior, and Sílvia Silva da Costa Botelho. "An Optical Character Recognition Post-processing Method for technical documents." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27464.

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Methods for correcting errors generated by Optical Character Recognition (OCR) system are being developed for a long time, with interesting results in their applications. However, these methods tend to work only on data with words that are part of an existing language and with a large semantic relationship between each word in the text. In this work, an error correction method is proposed that focuses on types of documents without these large semantic relationships inside their text. Instead, we focus on sparse text that tends to have little semantic relationship between the words found within
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Lauc, Davor. "PolyIPA - Multilingual Phoneme-to-Grapheme Conversion Model." In 6th International Conference on Natural Language Processing, Information Retrieval and AI. Academy & Industry Research Collaboration Center, 2025. https://doi.org/10.5121/csit.2024.150211.

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This paper presents PolyIPA, a novel multilingual phoneme-to-grapheme conversion model designed for multilingual name transliteration, onomastic research, and information retrieval. The model leverages two helper models developed for data augmentation: IPA2vec for finding soundalikes across languages, and similarIPA for handling phonetic notation variations. Evaluated on a test set that spans multiple languages and writing systems, the model achieves a mean Character Error Rate of 0.055 and a character-level BLEU score of 0.914, with particularly strong performance on languages with shallow or
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Velayuthan, P., and T. D. Ambegoda. "Benchmarking OCR models for Sinhala and Tamil document digitization." In Engineering Research Unit Symposium 2024. Engineering Research Unit, 2024. https://doi.org/10.31705/eru.2024.7.

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The digitization of documents in low-resource languages, such as Sinhala and Tamil, presents significant challenges due to the unique complexities of these scripts and the scarcity of high-quality training data. While traditional OCR systems have made strides in converting printed text to digital formats, they struggle with the intricate layouts and linguistic nuances of underrepresented languages. Recent advancements in Vision- Language Models (VLMs), like UDOP [1] and HRVDA [2], have integrated visual and textual data for improved document understanding. However, the application of these mod
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Kimura, Yoshimasa, Hiroyuki Nishi, and Eiich Mukai. "A reliability estimation method of character recognition using maximization of error-rejection rate." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5686555.

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Sawata, Ryosuke, Yosuke Kashiwagi, and Shusuke Takahashi. "Improving Character Error Rate is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-Box Acoustic Models." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746398.

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Szűcs, Kata Ágnes. "Automatikus kézírás-felismertetés Kiss József levelezésén." In Networkshop. HUNGARNET Egyesület, 2021. http://dx.doi.org/10.31915/nws.2021.8.

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The digital edition of the József Kiss correspondence is a pilot project of the Centre for Digital Humanities, Petőfi Literary Museum. In addition to the processing of the personal and professional letters of the 19th-century writer, poet, and editor of the literary journal A Hét (The Week), the project is to explore the possibilities offered by the Transkribus software. Handwritten Text Recognition is an emerging field of the digital humanities. The paper will discuss this artificial intelligence-based technology and our experiences in creating a Hungarian model. The best result has a 6,94% c
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Shanmugam, Ragavanantham, Muthuramalingam Thangaraj, Monsuru Ramoni, and Mahendra Gaikwad. "Enhancing the Performance Measures of Abrasive Water Jet Machining on Cutting Titanium Alloy Specimens." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-145330.

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Abstract The Titanium alloy is widely utilized in many bio medical application industries due its higher corrosion resistance and lower density. Since the Abrasive water jet machining (AWJM) process is used to cut such materials, it is essential to enhance the process mechanism as much as possible. The optimization of the AWJM technique can effectively improve the perfromance measures. In the present approach, an attempt was made to improve the performance measures of AWJM process with 40 mesh garnet particles while machining titanium (Ti-6Al-4V) specimens. The effect of input parameters such
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Saeki, Takaaki, Soumi Maiti, Xinjian Li, Shinji Watanabe, Shinnosuke Takamichi, and Hiroshi Saruwatari. "Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/575.

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While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text and studio-quality audio data. This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language. The use of text-only data allows the development of TTS systems for low-resource languages for which only textual resources are available, making TTS accessible to thousands of languages. Inspired by the strong cross-lingual transferability of multilingual language models, our frame
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Reports on the topic "Character Error Rate (CER)"

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Griffiths, Rachael. Transkribus in Practice: Improving CER. Verlag der Österreichischen Akademie der Wissenschaften, 2022. http://dx.doi.org/10.1553/tibschol_erc_cog_101001002_griffiths_cer.

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This paper documents ongoing efforts to enhance the accuracy of Handwritten Text Recognition (HTR) models using Transkribus, focusing on the transcription of Tibetan cursive (dbu med) manuscripts from the 11th to 13th centuries within the framework of the ERC-funded project, The Dawn of Tibetan Buddhist Scholasticism (11th-13th C.) (TibSchol). It presents the steps taken to improve the Character Error Rate (CER) of the HTR models, the results achieved so far, and considerations for those working on similar projects.
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