Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.

Drawing and Recognizing Chinese Characters with Recurrent Neural Network

Off-line signature verification based on grey level information using texture features

This paper reviews the state of the art in off-line Roman cursive handwriting recognition. The input provided to an off-line handwriting recognition system is an image of a digit, a word, or - more generally -some text, and the system produces, as output, an ASCII transcription of the input. This task involves a number of processing steps, some of which are quite difficult. Typically, preprocessing, normalization, feature extraction, classification, and postprocessing operations are required. We'll survey the state of the art, analyze recent trends, and try to identify challenges for future research in this field.

Recognition of cursive Roman handwriting: past, present and future

Off-line signature verification using structural feature correspondence

A biometric handwriting identification system converts characters and a writing sample into mathematical graphs. The graphs comprise enough information to capture the features of handwriting that are unique to each individual. Optical character recognition (OCR) techniques can then be used to identify these features in the handwriting sample so that drafts from two different samples can be aligned to compare to determine if the features in the writing sample correlate with each other.

Systems and methods for biometric identification using handwriting recognition

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.

M. Yasuhara

Papers

Recovery of drawing order from single-stroke handwriting images