S. Knerr

1.5k total citations
12 papers, 446 citations indexed

About

S. Knerr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, S. Knerr has authored 12 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 4 papers in Human-Computer Interaction. Recurrent topics in S. Knerr's work include Handwritten Text Recognition Techniques (10 papers), Natural Language Processing Techniques (5 papers) and Neural Networks and Applications (4 papers). S. Knerr is often cited by papers focused on Handwritten Text Recognition Techniques (10 papers), Natural Language Processing Techniques (5 papers) and Neural Networks and Applications (4 papers). S. Knerr collaborates with scholars based in France, Malaysia and Switzerland. S. Knerr's co-authors include Gérard Dreyfus, L. Personnaz, Christian Viard-Gaudin, David J. Price, E. Augustin, Yong Haur Tay, Horst Bunke, Marcus Liwicki, James A. Pittman and Muhammad Irfan Khalid and has published in prestigious journals such as Pattern Recognition Letters, Computer Vision and Image Understanding and Machine Vision and Applications.

In The Last Decade

S. Knerr

12 papers receiving 402 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
S. Knerr France	9	343	239	81	63	27	12	446
Kazuki Nakashima Japan	3	521 1.5×	214 0.9×	161 2.0×	39 0.6×	29 1.1×	10	607
Zinan Zeng Singapore	5	350 1.0×	268 1.1×	78 1.0×	29 0.5×	26 1.0×	6	461
Yasuji Miyake Japan	8	571 1.7×	184 0.8×	167 2.1×	26 0.4×	28 1.0×	12	627
Li-Lun Wang United States	5	151 0.4×	142 0.6×	45 0.6×	28 0.4×	17 0.6×	7	284
Rongchun Zhao China	8	198 0.6×	88 0.4×	67 0.8×	16 0.3×	42 1.6×	57	321
Fatoş T. Yarman-Vural Türkiye	9	603 1.8×	177 0.7×	295 3.6×	46 0.7×	22 0.8×	32	668
Mou-Yen Chen United States	6	241 0.7×	151 0.6×	73 0.9×	28 0.4×	47 1.7×	8	323
Constantinos Constantinopoulos Greece	7	123 0.4×	198 0.8×	25 0.3×	25 0.4×	19 0.7×	14	299
Mahdi Jampour Iran	10	245 0.7×	78 0.3×	58 0.7×	16 0.3×	37 1.4×	35	326
O. Matan United States	5	171 0.5×	139 0.6×	40 0.5×	15 0.2×	25 0.9×	8	265

Countries citing papers authored by S. Knerr

Since Specialization

Citations

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

Fields of papers citing papers by S. Knerr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Knerr

This figure shows the co-authorship network connecting the top 25 collaborators of S. Knerr. A scholar is included among the top collaborators of S. Knerr 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 S. Knerr. S. Knerr 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:

12 of 12 papers shown

1.

Liwicki, Marcus, Horst Bunke, James A. Pittman, & S. Knerr. (2009). Combining diverse systems for handwritten text line recognition. Machine Vision and Applications. 22(1). 39–51. 9 indexed citations

2.

Viard-Gaudin, Christian, et al.. (2005). Recognition-directed recovering of temporal information from handwriting images. Pattern Recognition Letters. 26(16). 2537–2548. 17 indexed citations

3.

Viard-Gaudin, Christian, et al.. (2004). From Off-line to On-line Handwriting Recognition. Data Archiving and Networked Services (DANS). 28 indexed citations

4.

Knerr, S. & E. Augustin. (2002). A neural network-hidden Markov model hybrid for cursive word recognition. 2. 1518–1520. 30 indexed citations

5.

Tay, Yong Haur, et al.. (2002). Offline handwritten word recognition using a hybrid neural network and hidden Markov model. 2. 382–385. 3 indexed citations

6.

Tay, Yong Haur, et al.. (2002). An analytical handwritten word recognition system with word-level discriminant training. 726–730. 11 indexed citations

7.

Knerr, S., L. Personnaz, & Gérard Dreyfus. (2002). A new approach to the design of neural network classifiers and its application to the automatic recognition of handwritten digits. i. 91–96. 2 indexed citations

8.

Viard-Gaudin, Christian, et al.. (1999). The IRESTE On/Off (IRONOFF) dual handwriting database. 125 indexed citations

9.

Knerr, S., et al.. (1998). Hidden Markov Model Based Word Recognition and Its Application to Legal Amount Reading on French Checks. Computer Vision and Image Understanding. 70(3). 404–419. 32 indexed citations

10.

Price, David J., S. Knerr, L. Personnaz, & Gérard Dreyfus. (1994). Pairwise Neural Network Classifiers with Probabilistic Outputs. 7. 1109–1116. 68 indexed citations

11.

Knerr, S., L. Personnaz, & Gérard Dreyfus. (1992). Handwritten digit recognition by neural networks with single-layer training. IEEE Transactions on Neural Networks. 3(6). 962–968. 116 indexed citations

12.

Saucier, G., et al.. (1991). Design and implementation of a dedicated neural network for handwritten digit recognition. 2. 63–67. 5 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.

Explore authors with similar magnitude of impact