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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Hossain, Md. Anwar
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Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Bangla handwritten word recognition using YOLO V5 Hossain, Md. Anwar; Abadin, AFM Zainul; Faruk, Md. Omar; Ara, Iffat; Rashidul Hasan, Mirza AFM; Fatta, Nafiul; Asraful, Md; Hossen, Ebrahim
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6953

Abstract

This research paper presents an innovative solution for offline handwritten word recognition in Bengali, a prominent Indic language. The complexities of this script, particularly in cursive writing, often lead to overlapping characters and segmentation challenges. Conventional methodologies, reliant on individual character recognition and aggregation, are error-prone. To overcome these limitations, we propose a novel method treating the entire document as a coherent entity and utilizing the efficient you only look once (YOLO) model for word extraction. In our approach, we view individual words as distinct objects and employ the YOLO model for supervised learning, transforming object detection into a regression problematic to predict spatially detached bounding boxes and class possibilities. Rigorous training results in outstanding performance, with remarkable box_loss of 0.014, obj_loss of 0.14, and class_loss of 0.009. Furthermore, the achieved mAP_0.5 score of 0.95 and map_0.5:0.95 score of 0.97 demonstrates the model’s exceptional accuracy in detecting and recognizing handwritten words. To evaluate our method comprehensively, we introduce the Omor-Ekush dataset, a meticulously curated collection of 21,300 handwritten words from 150 participants, featuring 141 words per document. Our pioneering YOLO-based approach, combined with the curated Omor-Ekush dataset, represents a significant advancement in handwritten word recognition in Bengali.
Enhanced human activity recognition through deep multi-layer perceptron on the UCI-HAR dataset Hossain, Md. Anwar; Ray, Sajeeb Kumar; Islam, Naima; Alamin, Alamin; Hasan, Mirza AFM Rashidul
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp429-438

Abstract

Using the UCI-HAR dataset, this paper examines human activity recognition (HAR) from the perspectives of data science and artificial intelligence. The primary objective is to present and evaluate the effectiveness of a multi-layer perceptron (MLP) model, concentrating on six different activity categories. We train and assess the MLP model using the UCI-HAR dataset, contrasting its results with those of convolutional neural networks (CNN). The MLP model shows competitive results, attaining an amazing 97% validation and testing accuracy, highlighting its efficiency for smaller datasets. An extensive study is carried out to assess the model's adaptation to a larger Motion Sense dataset using confusion matrices and cross-entropy, the model shows robustness with an accuracy of 89%. The MLP model performs admirably, demonstrating its capacity to pick up complex patterns. Results from comparative analysis with CNN are competitive, especially when dealing with smaller datasets. The suggested MLP model shows up as a practical and efficient way to advance HAR techniques. Its remarkable performance and versatility not only show its usefulness in real-world scenarios but also point to interesting directions for further study in the area of HAR.
Co-Authors Abadin, AFM Zainul alamin, Alamin Ara, Iffat Asraful, Md Faruk, Md. Omar Fatta, Nafiul Hasan, Mirza AFM Rashidul Hossen, Ebrahim Islam, Naima Rashidul Hasan, Mirza AFM Ray, Sajeeb Kumar