eprintid: 653 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/06/53 datestamp: 2022-05-06 23:55:11 lastmod: 2023-07-18 23:30:10 status_changed: 2022-05-06 23:55:11 type: article metadata_visibility: show creators_name: Singh, Tajinder Pal creators_name: Gupta, Sheifali creators_name: Garg, Meenu creators_name: Gupta, Deepali creators_name: Alharbi, Abdullah creators_name: Alyami, Hashem creators_name: Anand, Divya creators_name: Ortega-Mansilla, Arturo creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: arturo.ortega@uneatlantico.es creators_id: title: Visualization of Customized Convolutional Neural Network for Natural Language Recognition ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: Gurumukhi script; word recognition; convolutional neural network; performance analysis abstract: For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. date: 2022-04 publication: Sensors volume: 22 number: 8 pagerange: 2881 id_number: doi:10.3390/s22082881 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s22082881 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. metadata Singh, Tajinder Pal; Gupta, Sheifali; Garg, Meenu; Gupta, Deepali; Alharbi, Abdullah; Alyami, Hashem; Anand, Divya; Ortega-Mansilla, Arturo y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR (2022) Visualization of Customized Convolutional Neural Network for Natural Language Recognition. Sensors, 22 (8). p. 2881. ISSN 1424-8220 document_url: http://repositorio.unini.edu.mx/id/eprint/653/1/sensors-22-02881-v2.pdf