|Esprit Papyrus Review, Pozzuoli, september 1992: Nasser Sherkat (NTU), Bob Whitrow (NTU), Jean-Claude Simon (reviewer), Eberhard Mandler (reviewer), Eric Helsper (NICI), Lambert Schomaker (NICI), Gianpiero Meazza (Olivetti), Francesco Andreana (Olivetti). Picture taken by Hans-Leo Teulings (NICI). Not visible: Pietro Morasso (UGDIST). At the review, three on-line recognizers of isolated words (NTU,UGDIST,NICI), were tested together and in isolation. Nasser wrote a Borda-count rank combiner. Timothy Scanlan demonstrated a stand-alone recognizer by Digital (not visible). The NICI and UGDIST recognizers were trainable. The tablet is a monochrome Wacom HD series digitizer/LCD screen. The application was a Pen-Windows 3.1 program, sending the on-line XY coordinates of a word to a 486 (sic: powerful!) running an Unix version by Olivetti (IBISys). The recognition servers on Unix sent their word lists to the combiner which returned the average-rank hit list back to the Windows client. At the application level the goal was to illustrate that it would ultimately be possible to design pen-based computers in a hospital emergency-entrance context (Jon Kennedy, CAPTEC). Therefore, the word lexicon consisted of about 7000 medical terms from the targeted hospital context (St. James's Hospital, Dublin).|
Top-1 recognition (% words correct)
Top-5 recognition (%words correct)
At the time, the conclusion was that writing styles are so diverse that trainable recognizers (UGDIST, NICI) are at an advantage in free-style handwriting recognition. For example, the pre-training top-word performance on writer i was 3% for the NICI recognizer. After training, which consisted of labeling letters of 64 unrecognized words in the training set, a top-word recognition of 58% (See table) was achieved for this difficult Italian writer on the test set.
The static and rule-based systems (NOTPOLY, DIGITAL) performed very well on the style they were designed for.
...We knew since 1990 that HMM's probably would be able to solve this latter problem, but we did not spend efforts in this area, which was being explored by Yann le Cun who introduced the hybrid HMM/convolutional TDNN. Later, Stefan Manke made us regret our lazyness at the 1995 ICDAR, with his very good HMM/convolutional TDNN recognizer (NPen++).
Both the UGDIST and NICI recognizers relied on equidistant time sampling, in order to be able to compute velocities. With a DOS client, this was no problem. Under Windows, it proved to be a hassle. Live echoing of ink at 100 coordinates per second is not what a 25 MHz 486 running Windows 3.1 could handle easily. The NOTPOLY and DIGITAL recognizers were less upset by variations in sampling rate.
|Normalizations (pre-feature calculation)|
|hard-coded style assumptions||X||X||-||-|
|allow & use presence of i,j dots||X||-||X||X|
|allow & use presence of deferred t bars||X||X||X||-|
|tested max. size (kWords)||70||40||10||10|
|use of lexicon in recognition||post||embedded||post||post|
|memory footprint, inc. dictionary||0.5 MB||250 kB||1 MB||2.5 MB|
After the Esprit review, the conclusion for our NICI recognizer was that we definitely needed to add code for the deferred t-bar crossing, and that handling isolated characters as a special case would pay off. Before that, we had treated all script styles as chains of strokes. In case of isolated handprint, however, there may be "rubble" strokes between the characters, due to pen switching or all kinds of embellishing ligatures which are not present in connected cursive. Undoubtedly the other project partners had their own learning moments on the basis of the review process.
Today: the automatic recognition of on-line contemporary script and off-line historical script remains a tremendous challenge in science and technology research.