Preprint announcement

[Yoram Singer]

            *************** PAPERS AVAILABLE ****************
            ***     DO NOT FORWARD TO ANY OTHER LISTS     ***
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The following papers have been placed in cs.huji.ac.il (132.65.16.10).
The files are vmm.ps.Z and cursive.ps.Z . Ftp instructions follow the
abstracts. These are preprints of the papers to appear in the NIPS 6
proceedings.


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                    Decoding Cursive Scripts

                 Yoram Singer and Naftali Tishby
                Institute of Computer Science and
                  Center for Neural Computation
            Hebrew University, Jerusalem 91904, Israel

                            ABSTRACT:

Online  cursive  handwriting  recognition is  currently one  of the most
intriguing  challenges in  pattern  recognition.  This study  presents a
novel approach  to this  problem  which is composed of two complementary
phases. The  first is  dynamic encoding of  the  writing trajectory into
a compact sequence  of discrete  motor control symbols.  In this compact
representation  we largely  remove  the redundancy  of the script, while
preserving  most of  its intelligible  components.  In  the second phase
these control sequences are used to train adaptive probabilistic acyclic
automata (PAA) for the important ingredients of the writing trajectories
e.g. letters. We present a new and efficient learning algorithm for such
stochastic a automata,  and  demonstrate its utility  for  spotting  and
segmentation of cursive scripts.  Our experiments show that over  90% of
the  letters are  correctly spotted and identified,  prior to any higher
level  language  model.  Moreover,  both  the training  and  recognition
algorithms are very efficient compared to other modeling methods and the
models are `on-line' adaptable to other writers and styles.




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                      The Power of Amnesia

             Dana Ron    Yoram Singer   Naftali Tishby
                Institute of Computer Science and
                  Center for Neural Computation
            Hebrew University, Jerusalem 91904, Israel

                            ABSTRACT:

We  propose a learning algorithm for a  variable  memory  length  Markov
process.  Human communication,  whether given as text,  handwriting,  or
speech, has  multi  characteristic  time scales.  On short  scales it is
characterized mostly by the dynamics that generate the process,  whereas
on large scales, more syntactic and semantic information is carried. For
that reason the  conventionally used  fixed memory  Markov models cannot
capture effectively the complexity of such structures. On the other hand
using long  memory models  uniformly is not  practical even for as short
memory  as four.  The algorithm  we propose is  based on  minimizing the
statistical  prediction error by  extending the memory, or state length,
adaptively,  until the total prediction error is sufficiently small.  We
demonstrate the algorithm by learning the structure of  natural  English
text and applying the learned model to the correction of corrupted text.
Using less than  3000  states the model's performance is far superior to
that of fixed memory models with similar number of states.  We also show
how the algorithm can be applied to intergenic E.coli DNA base prediction
with results comparable to HMM-based methods.

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                        FTP INSTRUCTIONS

unix> ftp cs.huji.ac.il (or 132.65.16.10)
Name: anonymous
Password: your_full_email_address
ftp> cd singer
ftp> binary
ftp> get vmm.ps.Z
ftp> get cursive.ps.Z
ftp> quit
unix> uncompress vmm.ps.Z cursive.ps.Z
unix> lpr -P<printer-name> vmm.ps cursive.ps