This paper describes the system for the recognition of French handwriting submitted by A2iA to the competition organized at ICDAR2011 using the Rimes database.
This system is composed of several recognizers based on three different recognition technologies, combined using a novel combination method.
A framework multi-word recognition based on weighted finite state transducers is presented, using an explicit word segmentation, a combination of isolated word recognizers and a language model.
The system was tested both for isolated word recognition and for multi-word line recognition and submitted to the RIMES-ICDAR2011 competition.
This system outperformed all previously proposed systems on these tasks.
We present in this paper an HMM-based recognizer for the recognition of unconstrained Arabic handwritten words.
The recognizer is a context-dependent HMM which considers variable topology and contextual information for a better modeling of writing units.
We propose an algorithm to adapt the topology of each HMM to the character to be modeled.
For modeling the contextual units, a state-tying process based on decision tree clustering is introduced which significantly reduces the number of parameters.
Decision trees are built according to a set of expert-based questions on how characters are written.
Questions are divided into global questions yielding larger clusters and precise questions yielding smaller ones.
We apply this modeling to the recognition of Arabic handwritten words.
Experiments conducted on the OpenHaRT2010 database show that variable length topology and contextual information significantly improves the recognition rate.
This paper presents an HMM-based recognizer for the off-line recognition of handwritten words. Word models
are the concatenation of context-dependent character models (trigraphs). The trigraph models we consider are
similar to triphone models in speech recognition, where a character adapts its shape according to its adjacent
characters. Due to the large number of possible context-dependent models to compute, a top-down clustering is
applied on each state position of all models associated with a particular character. This clustering uses decision
trees, based on rhetorical questions we designed. Decision trees have the advantage to model untrained trigraphs.
Our system is shown to perform better than a baseline context independent system, and reaches an accuracy
higher than 74% on the publicly available Rimes database.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.