Penerapan Optical Character Recognition (OCR) Dengan Text-To-Speech (TTS) dalam Konversi Gambar ke Suara
), Ibnu Mas'ud Luthfi(2), M. Nur Fu'ad(3), M. Mujiono -(4), (1) Akademi Komunitas Negeri Putra Sang Fajar Blitar
Indonesia
(2) STAI At-Tahdzib Jombang
Indonesia
(3) Akademi Komunitas Negeri Putra Sang Fajar Blitar
Indonesia
(4) Akademi Komunitas Negeri Putra Sang Fajar Blitar
Indonesia
Corresponding Author
DOI : https://doi.org/10.24036/voteteknika.v11i4.125218
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Abstract
Aksesibilitas informasi menjadi perhatian utama untuk memastikan bahwa semua individu dapat mengakses dan memahami konten secara maksimal Gangguan penglihatan menjadi salah satu disabilitas atau kekurangan yang cukup banyak dialami oleh orang Indonesia yang dalam perkembangannya menimbulkan berbagai masalah sebagai akibat dari kekurangan yang dimiliki salah satunya adalah aksebilitas informasi. Penelitian ini secara tidak langsung output yang dihasilkan merupakan hasil pengabungan dari menggunakan Optical Character Recognition dengan konversi representasi Vector Quantized Variational Autoencoder dengan pengubah suara Text-to-Speech dari google (gTTS) yang dilakukan sebagai upaya untuk menghasilkan kualitas suara yang lebih baik dan alami serta mempertahankan informasi asli. Hasil pengujian dalam penelitian diperoleh akurasi konversi dan pengubahan sebanyak 83,33% dengan 10 data uji dapat dikonversi dan diubah dengan baik dan cukup efektif dalam mempertahankan informasi asli dan menghasilkan suara natural.
Kata kunci : Akses Informasi; Gangguan Penglihatan; OCR; VQ-VAE; gTTS; Machine Learning
Accessibility to information is a major concern to ensure that all individuals can access and understand content to the fullest. Impaired vision is one of the disabilities or deficiencies experienced by quite a lot of Indonesians, which in its development creates various problems as a result of the deficiencies they have, one of which is information accessibility. This research indirectly produces the output that is the result of a combination of using Optical Character Recognition with the conversion of the Vector Quantized Variational Autoencoder representation with the Text-to-Speech voice modifier from Google (gTTS) which is carried out as an effort to produce better and more natural voice quality and retain original information. The test results in this study obtained an accuracy of conversion and conversion of 83.33% with 10 test data that can be converted and changed properly and are quite effective in retaining original information and producing natural sound.
Keywords: Information Access; Visual Impairment; OCR; VQ-VAE; gTTS; Machine Learning
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