M. Schenkel

408 total citations
11 papers, 254 citations indexed

About

M. Schenkel is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, M. Schenkel has authored 11 papers receiving a total of 254 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Human-Computer Interaction. Recurrent topics in M. Schenkel's work include Handwritten Text Recognition Techniques (8 papers), Image Processing and 3D Reconstruction (4 papers) and Natural Language Processing Techniques (4 papers). M. Schenkel is often cited by papers focused on Handwritten Text Recognition Techniques (8 papers), Image Processing and 3D Reconstruction (4 papers) and Natural Language Processing Techniques (4 papers). M. Schenkel collaborates with scholars based in Switzerland, United States and Australia. M. Schenkel's co-authors include Isabelle Guyon, D. Henderson, G.S. Moschytz, Horst Bunke, Simon Carlile, M.A. Jabri, Craig Jin, W. Wilkening, Pirkko Pfäffli and Wolf Fïchtner and has published in prestigious journals such as The Journal of the Acoustical Society of America, Pattern Recognition and Pattern Recognition Letters.

In The Last Decade

M. Schenkel

11 papers receiving 222 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. Schenkel Switzerland	10	185	112	56	50	26	11	254
Nikolaos Gkalelis Greece	10	344 1.9×	146 1.3×	38 0.7×	36 0.7×	21 0.8×	29	403
Jinhee Chun Japan	6	157 0.8×	48 0.4×	34 0.6×	29 0.6×	8 0.3×	19	235
Alex Waibel United States	8	206 1.1×	122 1.1×	59 1.1×	38 0.8×	69 2.7×	18	340
Shengye Yan China	6	224 1.2×	30 0.3×	29 0.5×	59 1.2×	39 1.5×	15	269
Gräf Canada	3	230 1.2×	78 0.7×	21 0.4×	9 0.2×	49 1.9×	3	289
Horst Eidenberger Austria	11	253 1.4×	44 0.4×	83 1.5×	29 0.6×	14 0.5×	44	319
Mert Dikmen United States	7	208 1.1×	46 0.4×	27 0.5×	14 0.3×	10 0.4×	9	230
S.X. Ju Canada	4	393 2.1×	44 0.4×	36 0.6×	63 1.3×	13 0.5×	5	429
M. Yasuhara Japan	10	218 1.2×	80 0.7×	18 0.3×	69 1.4×	35 1.3×	23	282
Zhiming Liu China	7	142 0.8×	25 0.2×	26 0.5×	26 0.5×	41 1.6×	30	215

Countries citing papers authored by M. Schenkel

Since Specialization

Citations

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

Fields of papers citing papers by M. Schenkel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Schenkel

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

11 of 11 papers shown

1.

Bunke, Horst, et al.. (2002). Recovery of temporal information of cursively handwritten words for on-line recognition. 2. 931–935. 21 indexed citations

2.

Schenkel, M., Isabelle Guyon, & D. Henderson. (2002). On-line cursive script recognition using time delay neural networks and hidden Markov models. ii. II/637–II/640. 18 indexed citations

3.

Schenkel, M., et al.. (2001). Substrate potential shift due to parasitic minority carrier injection in smart-power ICs: measurements and full-chip 3D device simulation. Microelectronics Reliability. 41(6). 815–822. 1 indexed citations

4.

Jin, Craig, M. Schenkel, & Simon Carlile. (2000). Neural system identification model of human sound localization. The Journal of the Acoustical Society of America. 108(3). 1215–1235. 18 indexed citations

5.

Bunke, Horst, et al.. (1999). Online handwriting data acquisition using a video camera. 573–576. 16 indexed citations

6.

Schenkel, M. & M.A. Jabri. (1998). Low resolution, degraded document recognition using neural networks and hidden Markov models. Pattern Recognition Letters. 19(3-4). 365–371. 12 indexed citations

7.

Schenkel, M., et al.. (1996). Off-line cursive handwriting recognition compared with on-line recognition. 505–509 vol.4. 35 indexed citations

8.

Schenkel, M., et al.. (1995). Neural network filters for speech enhancement. IEEE Transactions on Speech and Audio Processing. 3(6). 433–438. 24 indexed citations

9.

Schenkel, M., Isabelle Guyon, & D. Henderson. (1995). On-line cursive script recognition using time-delay neural networks and hidden Markov models. Machine Vision and Applications. 8(4). 215–223. 81 indexed citations

10.

Schenkel, M., et al.. (1994). Recognition-based segmentation of on-line run-on handprinted words: Input vs. output segmentation. Pattern Recognition. 27(3). 405–420. 15 indexed citations

11.

Schenkel, M., et al.. (1992). Recognition-based Segmentation of On-Line Hand-printed Words. Neural Information Processing Systems. 5. 723–730. 13 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