Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

Focus prosody by Korean learners of English

This study examined whether Korean learners of English attained native-like performance in English focus prosody by conducting production and perception experiments using digit strings. Language learners were classified into advanced-, intermediate-, and low-level groups according to their proficiency and compared with native speakers. Native speakers’ focus prosody was clearly prominent in the focus positions, and their post-focus positions were considerably compressed. Their focused digits were easy to detect, resulting in a 97% identification rate. Although advanced-level speakers produced acoustic cues quite similar to those of native speakers, their post-focus production did not resemble that of native speakers. Their identification rate was 81%, 16% lower than that of native speakers. Neither intermediate- nor low-level speakers’ focus-cueing changes were distinguished whatsoever in the focus and post-focus positions. Their identification rates were just over 10%, similar to the level of chance in a 10-digit string, implying that their focus productions were not sufficiently salient to be recognized in the experiment. The results suggest that second language acquisition is hindered by a negative transfer between English and Korean. The acquisition of second language focus prosody proceeds slowly; second language learners approach native-like proficiency once they become advanced.

Prototipe Aplikasi Identifikasi Otomatis Pelaku Pengetap BBM Berbasis Rekognisi Citra Plat Nomor Kendaraan di Kota Tarakan

Penelitian ini mengusulkan pengembangan aplikasi identifikasi otomatis pelaku pengetap BBM bersubsidi di Kota Tarakan menggunakan rekognisi citra plat nomor kendaraan. Caranya adalah citra plat nomor kendaraan ditangkap secara real-time menggunakan kamera CCTV. Kemudian citra tersebut diekstrak menggunakan pustaka OpenALPR agar diperoleh rekognisi digit-digit string plat nomor. Data string plat nomor inilah yang selanjutnya menjadi bahan analisis sistem untuk mengidentifikasi adanya indikasi pelaku pengetap/pengisian berulang-ulang BBM bersubsidi atau tidak. Dalam skenario uji coba, dilakukan pengambilan data primer sebanyak 30 data citra bergerak/video kendaraan bermotor roda dua untuk kemudian diolah menggunakan sistem usulan dan hasilnya diuji dengan metode pengujian akurasi. Berdasarkan pengujian sistem usulan, diperoleh rata-rata hasil akurasi untuk mengubah 30 citra real-time/bergerak kendaraan bermotor ke dalam data string plat nomor sebesar 73,3%, dengan rata-rata banyaknya pendeteksian yang dilakukan per citra adalah 2,8 kali. Sedangkan hasil akurasi sistem dalam mengidentifikasi data string plat nomor ganda sebesar 76,7%. Hasil ini menunjukkan bahwa sistem usulan cukup akurat dan efisien sebagai sistem yang dapat digunakan untuk mengidentifikasi plat nomor kendaraan ganda sebagai indikasi adanya kandidat pelaku pengetap BBM.