Mustapha Amrouch

573 total citations · 1 hit paper
20 papers, 262 citations indexed

About

Mustapha Amrouch is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Mustapha Amrouch has authored 20 papers receiving a total of 262 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 8 papers in Media Technology and 2 papers in Artificial Intelligence. Recurrent topics in Mustapha Amrouch's work include Handwritten Text Recognition Techniques (15 papers), Vehicle License Plate Recognition (8 papers) and Image Processing and 3D Reconstruction (7 papers). Mustapha Amrouch is often cited by papers focused on Handwritten Text Recognition Techniques (15 papers), Vehicle License Plate Recognition (8 papers) and Image Processing and 3D Reconstruction (7 papers). Mustapha Amrouch collaborates with scholars based in Morocco and France. Mustapha Amrouch's co-authors include Youssef Es-Saady, Driss Mammass, Lahcen Omari and Faissal El Bouanani and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and International Journal of Pattern Recognition and Artificial Intelligence.

In The Last Decade

Mustapha Amrouch

20 papers receiving 241 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review

2023 140 citations Mustapha Amrouch et al. IEEE Access profile →

Peers

Mustapha Amrouch

CVPR

MT

AI

RNMI

AE

Dan Yao China

Zhize Wu China

M. Radha India

Philomina Simon India

Mahmoud Emam Egypt

Shuzhou Sun China

Lu Leng China

Chun-Ming Tsai Taiwan

Samy Ait-Aoudia Algeria

Dan Yao China

Mustapha Amrouch

151 ×0.9

CVPR

99 ×1.3

MT

58 ×0.9

AI

15 ×1.0

RNMI

13 ×1.1

AE

Citations per year, relative to Mustapha Amrouch Mustapha Amrouch (= 1×) peers Dan Yao

Countries citing papers authored by Mustapha Amrouch

Since Specialization

Citations

This map shows the geographic impact of Mustapha Amrouch's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mustapha Amrouch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mustapha Amrouch more than expected).

Fields of papers citing papers by Mustapha Amrouch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mustapha Amrouch. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mustapha Amrouch. The network helps show where Mustapha Amrouch may publish in the future.

Co-authorship network of co-authors of Mustapha Amrouch

This figure shows the co-authorship network connecting the top 25 collaborators of Mustapha Amrouch. A scholar is included among the top collaborators of Mustapha Amrouch based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mustapha Amrouch. Mustapha Amrouch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Amrouch, Mustapha, et al.. (2023). Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review. IEEE Access. 11. 35479–35516. 140 indexed citations breakdown →

2.

Bouanani, Faissal El, et al.. (2022). Strong consistency estimators of the Brennan‐Schwartz diffusion process based on martingales approach. Stat. 11(1). 1 indexed citations

3.

Amrouch, Mustapha, et al.. (2022). Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression. IEEE Access. 10. 128543–128553. 8 indexed citations

4.

Amrouch, Mustapha, et al.. (2021). Automatic Generation of Chest X-ray Reports Using a Transformer-based Deep Learning Model. 1–5. 9 indexed citations

5.

Amrouch, Mustapha, et al.. (2019). An Efficient Arabic HMM System Based on Convolutional Features Learning. 28. 1–5. 1 indexed citations

6.

Amrouch, Mustapha, et al.. (2018). Cursive Arabic Handwriting Recognition System Without Explicit Segmentation Based on Hidden Markov Models. SHILAP Revista de lepidopterología. Special Issue on Scientific.... 7 indexed citations

7.

Amrouch, Mustapha, et al.. (2017). The Relevant CNNs Features Based HMM for Arabic Handwriting Recognition. 17(3). 98–109. 2 indexed citations

8.

Amrouch, Mustapha, et al.. (2017). Improved HMM for Cursive Arabic Handwriing Recognition System using MLP Classifier. Transactions on Machine Learning and Artificial Intelligence. 5(4). 1 indexed citations

9.

Amrouch, Mustapha, et al.. (2017). Contextual Arabic Handwriting Recognition System using Embedded Training based Hybrid HMM/MLP Models. Transactions on Machine Learning and Artificial Intelligence. 5(4). 3 indexed citations

10.

Amrouch, Mustapha, et al.. (2017). Recognition of Cursive Arabic Handwritten Text Using Embedded Training Based on Hidden Markov Models. International Journal of Pattern Recognition and Artificial Intelligence. 32(1). 1860007–1860007. 19 indexed citations

11.

Amrouch, Mustapha, et al.. (2017). Recognition of cursive Arabic handwritten text using embedded training based on HMMs. SHILAP Revista de lepidopterología. 5(2). 245–251. 25 indexed citations

12.

Amrouch, Mustapha, et al.. (2017). Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition. 5 indexed citations

13.

Amrouch, Mustapha, et al.. (2017). A Survey of Contextual Handwritten Recognition Systems based HMMs for Cursive Arabic and Latin Script. International Journal of Computer Applications. 160(2). 31–37. 2 indexed citations

14.

Amrouch, Mustapha, et al.. (2016). An improved Arabic handwritten recognition system using embedded training based on HMMs. 77. 1–5. 4 indexed citations

15.

Es-Saady, Youssef, et al.. (2014). Amazigh Characters Automatic Recognition: Overview and Prospects. 2 indexed citations

16.

Es-Saady, Youssef, et al.. (2014). Handwritten Tifinagh Character Recognition Using Baselines Detection Features. 7 indexed citations

17.

Amrouch, Mustapha, et al.. (2014). Handwritten Amazigh Character Recognition Based On Hidden Markov Models. 7 indexed citations

18.

Amrouch, Mustapha, et al.. (2012). A Novel Feature Set for Recognition of Printed Amazigh Text using Maximum Deviation and HMM. International Journal of Computer Applications. 44(12). 23–30. 6 indexed citations

19.

Amrouch, Mustapha. (2012). Reconnaissance de caractères imprimés et manuscrits,textes et documents basée sur les modèles de Markov cachés. 1 indexed citations

20.

Amrouch, Mustapha, et al.. (2009). Printed amazigh character recognition by a hybrid approach based on Hidden Markov Models and the Hough transform. 2. 356–360. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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