Bpti: Bilingual (Arabic/English) Printed Text Images Dataset for Recognition Research

2022 ◽  
Author(s):  
Mohammad H. Yahia ◽  
Husni Al-Muhtaseb
Keyword(s):  
Printed Text ◽  
Text Images

scholarly journals Photometric Ligature Extraction Technique for Urdu Optical Character Recognition

2021 ◽  
Vol 11 (6) ◽  
pp. 7968-7973
Author(s):  
M. Kazmi ◽  
F. Yasir ◽  
S. Habib ◽  
M. S. Hayat ◽  
S. A. Qazi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Holistic Approach ◽  
Extraction Technique ◽  
Connected Component ◽  
Font Size ◽  
Optical Character ◽  
Segmentation Algorithms ◽  
Printed Text ◽  
Text Images

Urdu Optical Character Recognition (OCR) based on character level recognition (analytical approach) is less popular as compared to ligature level recognition (holistic approach) due to its added complexity, characters and strokes overlapping. This paper presents a holistic approach Urdu ligature extraction technique. The proposed Photometric Ligature Extraction (PLE) technique is independent of font size and column layout and is capable to handle non-overlapping and all inter and intra overlapping ligatures. It uses a customized photometric filter along with the application of X-shearing and padding with connected component analysis, to extract complete ligatures instead of extracting primary and secondary ligatures separately. A total of ~ 2,67,800 ligatures were extracted from scanned Urdu Nastaliq printed text images with an accuracy of 99.4%. Thus, the proposed framework outperforms the existing Urdu Nastaliq text extraction and segmentation algorithms. The proposed PLE framework can also be applied to other languages using the Nastaliq script style, languages such as Arabic, Persian, Pashto, and Sindhi.

scholarly journals Hardware and Software Co-Design of Arabic Alphabets Recognition Platform for Blind and Visually Impaired Persons

2017 ◽  
Vol 11 (1) ◽  
pp. 193-200
Author(s):  
Brahim Sabir ◽  
Yassine Khazri ◽  
Mohamed Moussetad ◽  
Bouzekri Touri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Visually Impaired ◽  
Recognition Algorithm ◽  
Hardware Platform ◽  
Blind And Visually Impaired ◽  
Optical Character ◽  
Printed Text ◽  
Visually Impaired Persons ◽  
Text Images

Background:Optical character Recognition (OCR) is a technic that converts scanned or printed text images into editable text. Many OCR solutions have been proposed and used for Latin and Chinese alphabets.However not much can be found about OCRs for the handwriting scripts Arabic Alphabets, and especially to be used for blind and visually impaired persons.This paper has been an attempt towards the development of an OCR for Arabic Alphabets dedicated to blind and visually impaired persons.Method:The proposed Optical Arabic Alphabets Recognition algorithm includes binarization of the inputted image, segmentation, feature extraction and a classification based on neural networks to match read Arabic alphabets with trained pattern.The proposed algorithm has been developed using Matlab, and the solution was designed to be implemented on hardware platform and can be customized for mobile phones.Conclusion:The presented method has the benefit that the accuracy of recognition is comparable to other OCR algorithms.

scholarly journals A Robot Object Recognition Method Based on Scene Text Reading in Home Environments

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1919
Author(s):  
Shuhua Liu ◽  
Huixin Xu ◽  
Qi Li ◽  
Fei Zhang ◽  
Kun Hou
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Multiple Objects ◽  
Recognition Method ◽  
Home Environments ◽  
Complex Scenes ◽  
Scene Text ◽  
Text Reading ◽  
Text Images ◽  
Chinese And English

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.

Sign in / Sign up

Export Citation Format

Share Document