Improved Neural Based Writer Adaptation for On-Line Recognition Systems

Abstract

The adaptation module is a Radial Basis Function Neural Network (RBF-NN) that can be connected to the output of any recognition system and its aim is to examine the output of the writer-independent system and produce a more correct output vector close to the desired response. The proposed adaptation module is built using an incremental training named GA-AM algorithm (Growing-Adjustment Adaptation Module). Two adaptation strategies are applied : Growing and Adjustment. The growing criteria are based on the estimation of the significance of the new input and the significance of the nearest unit compared to the input. The adjustment consists of the update of two specific units (nearest and desired contributor) parameters using the standard LMS gradient descent to decrease the error at each time no new unit is allocated. This new training algorithm is evaluated by the adaptation of two handwriting recognition systems. The results, reported according to the cumulative error, show that the GA-AM algorithm leads to decreasing the classification error and to instantly adapting the recognition system to a specific user's handwriting. Performance comparison of GA-AM training algorithm with two other adaptation strategies, based on four writer-dependent datasets, are presented.

OHRS-MEWA: On-Line Handwriting Recognition System with Multi-environment Writer Adaptation

“…In This section we confront our writer adaptation using IGA-AM algorithm with other algorithm GA-AM which is presented in [8]. The comparison not only involves the overall performance of the system, but also the system complexity (number of hidden units allocated in the adaptation module).…”

“…This step represents the improvement of the GA-AM algorithm [8]. It optimize the adaptation module's structure by acting on the number of hidden units allocated, consequently it acts also on error rate reduction.…”

“…The description and results using these algorithms are presented in [1], [7], [8] respectively. In this section we present a comparative results between the IGA-AM algorithm which is an improvement of the GA-AM algorithm [8].…”

“…Also we used the Euclidian distance to caculate the distance between unit centers and inputs. For the memory size (M) we referred to the work [8] where a study is made to determine the best memory size that minimizes the number of error and optimises the number of hidden units. As a result, the memory size is fixed to = 40.…”

“…There have been great researches on the field of writer adaptation which can be classified in three groups: systems reorganizing the prototypes of the database (addition, modification and deletion) [10], [11], [12], systems updating the parameters of the recognition system [15], [14], [13], [16], [6] and systems adapting without modifying the classifier's parameters [7], [8], [1], [5].…”