The IRESTE On/Off (IRONOFF) Dual Handwriting Database
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Combining Online and Offline Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Spontaneous Handwriting Recognition and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
IAM-OnDB - an On-Line English Sentence Database Acquired from Handwritten Text on a Whiteboard
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Writing Speed Normalization for On-Line Handwritten Text Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Computer Assisted Transcription of Handwritten Text Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Computer Assisted Transcription of Text Images and Multimodal Interaction
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
A Bi-modal Handwritten Text Corpus: Baseline Results
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An iterative multimodal framework for the transcription of handwritten historical documents
Pattern Recognition Letters
| Hi-index | 0.00 |
Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the online modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of old documents, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to provide the Community with an useful corpus to make easy tests, and to establish baseline performance figures, we have proposed this handwritten bi-modal contest. Here is reported the results of the contest with two participants, one of them achieved a 0% classification error rate, whilst the other participant achieved an interesting 1.5%.