Raid Saabni

421 total citations
25 papers, 290 citations indexed

About

Raid Saabni is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Raid Saabni has authored 25 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 6 papers in Media Technology. Recurrent topics in Raid Saabni's work include Handwritten Text Recognition Techniques (18 papers), Image Retrieval and Classification Techniques (7 papers) and Natural Language Processing Techniques (7 papers). Raid Saabni is often cited by papers focused on Handwritten Text Recognition Techniques (18 papers), Image Retrieval and Classification Techniques (7 papers) and Natural Language Processing Techniques (7 papers). Raid Saabni collaborates with scholars based in Israel, United States and Germany. Raid Saabni's co-authors include Jihad El‐Sana, Fadi Biadsy, Alexander M. Bronstein, Alex Bronstein, Alon Schclar, Moti Zwilling, Tobias Friedrich, Tim Fingscheidt, Maximilian Schulze and Volker Märgner and has published in prestigious journals such as Pattern Recognition Letters, Applied Sciences and International Journal on Document Analysis and Recognition (IJDAR).

In The Last Decade

Raid Saabni

24 papers receiving 276 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Raid Saabni Israel 10 280 108 86 26 11 25 290
Ronaldo Messina France 7 227 0.8× 58 0.5× 148 1.7× 20 0.8× 28 2.5× 10 273
Apurva A. Desai India 9 227 0.8× 165 1.5× 91 1.1× 36 1.4× 5 0.5× 30 277
Mauricio Villegas Spain 12 318 1.1× 67 0.6× 108 1.3× 16 0.6× 48 4.4× 25 349
Najiba Tagougui Tunisia 9 256 0.9× 109 1.0× 118 1.4× 56 2.2× 8 0.7× 19 287
Abdeljalil Gattal Algeria 11 257 0.9× 51 0.5× 83 1.0× 52 2.0× 13 1.2× 32 280
Salvador España-Boquera Spain 6 242 0.9× 68 0.6× 136 1.6× 31 1.2× 24 2.2× 14 298
J. Gorbe-Moya Spain 5 210 0.8× 58 0.5× 97 1.1× 34 1.3× 25 2.3× 5 246
Abderrazak Zahour France 9 275 1.0× 164 1.5× 46 0.5× 10 0.4× 8 0.7× 15 285
Swapan Kumar Parui India 11 296 1.1× 179 1.7× 117 1.4× 51 2.0× 7 0.6× 26 347
Minesh Mathew India 11 372 1.3× 110 1.0× 219 2.5× 30 1.2× 13 1.2× 13 440

Countries citing papers authored by Raid Saabni

Since Specialization
Citations

This map shows the geographic impact of Raid Saabni'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 Raid Saabni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raid Saabni more than expected).

Fields of papers citing papers by Raid Saabni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Raid Saabni. 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 Raid Saabni. The network helps show where Raid Saabni may publish in the future.

Co-authorship network of co-authors of Raid Saabni

This figure shows the co-authorship network connecting the top 25 collaborators of Raid Saabni. A scholar is included among the top collaborators of Raid Saabni 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 Raid Saabni. Raid Saabni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Saabni, Raid, et al.. (2022). Understanding Unsupervised Deep Learning for Text Line Segmentation. Applied Sciences. 12(19). 9528–9528. 3 indexed citations
2.
Schulze, Maximilian, et al.. (2022). Optical character recognition guided image super resolution. 1–4.
3.
Schclar, Alon, et al.. (2021). Text line extraction using deep learning and minimal sub seams. 1–4. 1 indexed citations
4.
Saabni, Raid & Alon Schclar. (2020). Facial Expression Recognition Using combined Pre-Trained Convnets. 95–106. 4 indexed citations
5.
Saabni, Raid. (2018). Robust and Efficient Text. 1–6. 2 indexed citations
6.
Saabni, Raid. (2017). Boosting feature based classifiers for writer identification. 69. 99–103. 1 indexed citations
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Märgner, Volker, et al.. (2013). HADARA – A Software System for Semi-Automatic Processing of Historical Handwritten Arabic Documents. Archiving Conference. 10(1). 161–166. 3 indexed citations
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Saabni, Raid, et al.. (2013). Text line extraction for historical document images. Pattern Recognition Letters. 35. 23–33. 60 indexed citations
13.
Saabni, Raid. (2013). The multi angular descriptor (MAD). 53–58. 2 indexed citations
14.
Saabni, Raid & Alexander M. Bronstein. (2012). Fast Keyword Searching Using 'BoostMap' Based Embedding. 734–739. 7 indexed citations
15.
Saabni, Raid & Moti Zwilling. (2012). Text Detection and Recognition in Real World Images. 443–448. 1 indexed citations
16.
Saabni, Raid, et al.. (2012). Comprehensive synthetic Arabic database for on/off-line script recognition research. International Journal on Document Analysis and Recognition (IJDAR). 16(3). 285–294. 21 indexed citations
17.
Saabni, Raid & Alex Bronstein. (2011). Fast Key-Word Searching via Embedding and Active-DTW. 68–72. 6 indexed citations
18.
Saabni, Raid & Jihad El‐Sana. (2011). Language-Independent Text Lines Extraction Using Seam Carving. 563–568. 32 indexed citations
19.
Saabni, Raid, et al.. (2010). Word spotting for handwritten documents using Chamfer Distance and Dynamic Time Warping. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7874. 78740J–78740J. 10 indexed citations
20.
Saabni, Raid & Jihad El‐Sana. (2009). Hierarchical On-line Arabic Handwriting Recognition. 867–871. 18 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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