Multi-lingual handwriting recovery framework based on convolutional denoising autoencoder with attention model

Article Type

Research Article

Publication Title

Multimedia Tools and Applications

Abstract

For several decades, no satisfactory solutions have been provided to the problem of offline handwriting recognition. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static images has not been well explored yet. In this paper, we introduce a novel multi-lingual word handwriting recovery framework based on a convolutional denoising autoencoder with an attention model for pen up/down, velocity and temporal order recovery. The proposed framework consists of extracting robust features from a handwriting image using a stacked denoising autoencoder and an encoder Bidirectional Gated Recurrent Unit (BGRU) model. Then, the obtained vectors are decoded to produce an online script with dynamic characteristics using a BGRU with temporal attention. Evaluation is done on a Latin and Arabic Online and offline handwriting character / word databases and the proposed framework achieves high competitive results.

First Page

22295

Last Page

22326

DOI

10.1007/s11042-023-16499-z

Publication Date

3-1-2024

Comments

Open Access; Green Open Access

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