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Abstract

Python has developed into one of the most popular and powerful languages in coding and can be used for various purposes such as language detection. Language detection's idea is based on the identification of the characters used in words and expressions in the text. The main aim is to identify words commonly used in a particular language such as of or to in English. Python has several modules to enhance language identification. Language detection is a common feature in any Web application or Social Network, which is usually combined with Machine Translation to enhance content accessibility and user experience. This develops a platform for other features including document analysis, articles/tweets/posts, and Machine Translation. When the document is fed, the language is not known. As a result, conducting segmentation is not possible since the language in the document is unknown, which makes using word base models impossible. Consequently, the best option is the use of "Observa." One research area that aims to establish a computer system that can identify texts from images is text recognition in images. In today's world, there is a growing demand to transfer information from paper documents to the storage disks in computers for easier reuse and access of the information through the process of searching. A simple method of achieving this is through scanning the paper documents and then storing them in image form. However, it is extremely difficult to reuse the information by reading the contents and manually searching for what is needed from the images. Various challenges associated are poor image quality and the font of the document's characteristics. As a result of the challenges, the computer cannot identify the characters and read the document. The paper discusses various ways to recognize texts from images. The aim of this work is to identify texts from images to ensure that the reader can apply a certain structure of distinct processing modules for easier comprehension.

Keywords

detection processing comprehension characters

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Iman Kadhim Ajlan, Ahmed fadhil Bin yusof, Herlina Binti Abdul Rahim, & Ahmed Jameel Ismael. (2022). Text Recognition from Images. Texas Journal of Engineering and Technology, 10, 10–18. Retrieved from https://zienjournals.com/index.php/tjet/article/view/2098

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