Neural Networks for Handwriting Recognition - Computational Intelligence Paradigms in Advanced Pattern Classification - page 20

Table 1 Main results for online data

System Word Accuracy Character Accuracy

HMM 65.0% -

CTC (BLSTM)79.7% 88.5%

Table 2 Main results for offline data

System Word Accuracy Character Accuracy
HMM 64.5% -
CTC (BLSTM)74.1% 81.8%
As can be seen from Tables 1 and 2, the RNN substantially outperformed the
HMM on both databases. To put these results in perspective, the Microsoft tablet
PC handwriting recognizer [37] gave a word accuracy score of 71.32% on the on-
line test set. This result is not directly comparable to our own, since the Microsoft
system was trained on a different training set, and uses considerably more sophis-
ticated language modeling than the HMM and RNN systems we implemented.
However, it indicates that the RNN-based recognizer is competitive with the best
commercial systems for unconstrained handwriting.

4.2 Recognition Performance of MDLSTM on Contest' Data

The MDLSTM system participated in three handwriting recognition contests at the
ICDAR 2009 (see the proceedings in [38]). The recognition tasks were based on
different scripts. In all cases, the systems had to recognize handwriting from un-
known writers.

Table 3 Summarized results from the online Arabic handwriting recognition competition

System Word Accuracy Time/Image
REGIM HMM 52.67% 6402.24 ms

Vision Objects 98.99% 69.41 ms

CTC (BLSTM)95.70% 1377.22 ms

Table 4 Summarized results from the offline Arabic handwriting recognition competition

System Word Accuracy T ime/Image
Arab-Reader HMM 76.66% 2583.64 ms

Multi-Stream HMM74.51% 143269.81 ms

CTC (MDLSTM) 81.06% 371.61 ms