Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

TL;DR

This paper presents an end-to-end deep encoder-decoder network that recovers handwritten pen trajectories from offline character images, bridging the gap between online and offline handwriting recognition methods.

Contribution

It introduces a novel sequence-to-sequence model with convolutional LSTM for offline handwriting trajectory recovery, a new approach in document image analysis.

Findings

Achieved superior performance over conventional methods

Successfully recovered pen trajectories for Tamil, Telugu, and Devanagari characters

Demonstrated the effectiveness of end-to-end deep learning in offline handwriting analysis

Abstract

In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and offline methods. Our proposed framework employs sequence to sequence model which consists of an encoder-decoder LSTM module. Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation. The output of the encoder is fed to a decoder LSTM and we get the successive coordinate points from every time step of the decoder LSTM. Although the sequence to sequence model is a popular paradigm in various computer vision and language translation tasks, the main contribution of our work lies in designing an end-to-end network for a decade old popular problem in Document Image Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit dataset are used for our experiments. Our proposed method has achieved superior performance compared to the other conventional approaches.