Segmentation-Free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

Abstract

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten manuscripts. To tackle the problem, a segmentation-free recognition algorithm based on deep learning network is proposed in this paper. The network consists of four neural layers, including input layer for image preprocessing, convolutional neural networks (CNNs) layer for feature extraction, bidirectional long-short term network (BDLSTM) layer for sequence prediction, and connectionist temporal classification (CTC) layer for text sequence alignment and classification. Besides, a novel data processing method is performed for data length equalization. Based on this, groups of experiments, based on six typical databases, involved in evaluation indicators of character correct rate, training time cost, storage space cost, and testing time cost are carried out. The experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.