Combination of explicit segmentation with Seq2Seq recognition for fine analysis of children handwriting

We consider the task of analyzing children handwriting in the context of a dictation task. The objective is to detect orthographic and phonological errors. To achieve this goal, we extend an existing handwriting analysis engine, based on an explicit segmentation of the handwritten input, originally developed for children copying exercises. We present a new approach, based on the combination of this analysis engine with a deep learning word recognition approach in order to improve both the recognition and segmentation performance. Explicit segmentation needs prior knowledge, and the deep network recognition predictions are a reliable approximation of the ground truth which can guide the analysis process. We propose to combine multiple prior knowledge strategies to further improve the analysis performance. Furthermore, we exploit the deep network approximate implicit segmentation to optimize the existing analysis process in terms of complexity.

Automated summary

This study aims to improve the performance of children handwriting analysis by combining a handwriting analysis engine with deep learning word recognition methods. The analysis process is guided by prior knowledge and predictions from deep networks.