Implementation of image processing based Digital Dactylology Converser for deaf-mute persons

Abstract

Language of signs is usually used by the deaf-mute persons in order to convey their message. But, it becomes very difficult for those who have no knowledge of the sign language. The aim of this research work is to resolve this problem using Digital Dactylology Converser (DOC) which is a device that converts a sign language into voice signal and text message. Primarily a hands gesture is recognised by computer vision program and based on that image a speech and text is played and displayed on the speaker and LCD respectively. Purpose of this device is to make conversation easy between dumb and the normal people around the world. In order to minimize their limitations and enable them to expand their boundaries, this is best suitable device. Experimental results verify the operational accuracy of the device for different users. The proposed device can work well and translates the letters, alphabets to text and sound.

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“…Salah satu perspektif kunci saat berkomunikasi dengan penyandang tunarungu adalah pengenalan bahasa isyarat (Hamouda, 2021). Bahasa isyarat biasanya digunakan oleh penyandang tuna rungu untuk menyampaikan pesannya (Javed, 2016). Penyandang tunarungu mengandalkan bahasa isyarat untuk berkomunikasi dengan manusia lain (Golîmba, 2022).…”

Techno-talk: An American Sign Language (ASL) Translator

“…There are some limitations in vision based techniques which has to be considered before choosing this approach. In approach of Digital Image Processing, a camera has to be interfaced with a Single Board Computer or Laptop that can restrict an device to be wearable as compared to a glove [4]. And accuracy of an image processing algorithm can be reduced in case of classifying the signs that are similar in orientation or posture [5].…”

“…The corresponding output was displayed after the gesture was recognized by pattern recognition of the Neural Network. To increase accuracy of a neural network based approach, a large dataset of signs can contribute to increase accuracy of system [4] [5].…”