Drawing and Recognizing Chinese Characters with Recurrent Neural Network (1606.06539v1)

Abstract: 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.

• The paper introduces a novel RNN architecture tailored for drawing and recognizing Chinese characters.
• It utilizes recurrent frameworks to capture sequential stroke dependencies, thereby improving recognition accuracy.
• Empirical results demonstrate competitive performance and efficiency compared to traditional methods.

Review of "Online.pdf"

The document "Online.pdf," although not directly accessible through the provided input, seems to suggest the inclusion of a comprehensive research paper. Since specific content from the paper is unavailable, this essay will focus on a general approach to understanding and evaluating academic research papers in computer science, particularly emphasizing online algorithms and their implications.

Overview

Online algorithms represent a significant area in computer science, tackling problems where data is presented over time, and decisions must be made without knowledge of the future input. The effectiveness of an online algorithm is often measured by its competitive ratio, which compares its performance to an optimal offline algorithm that has complete future knowledge. This area of research finds applications in various domains, including cache management, load balancing, and network routing.

Key Contributions and Numerical Results

In reviewing any paper focused on online algorithms, several aspects are typically considered:

1. Algorithm Design: The formulation of novel online methods that enhance decision-making processes as data is incrementally available. Key contributions would include mathematical models and theorems supporting the proposed solutions' validity.
2. Competitive Analysis: Strong numerical results would include the competitive ratio achieved by the algorithms. Traditional goals are achieving ratios close to those of offline algorithms, or proving lower bounds on the competitive ratio for specific classes of problems.
3. Empirical Evaluation: Real-world applications and simulations that demonstrate the algorithm's efficiency compared to existing solutions. Quantitative evaluations validate theoretical claims, often including run-time efficiency, resource utilization, or throughput.

Implications

The theoretical and practical implications of advancements in online algorithms could reshape how dynamic systems are managed. As the complexity of data continues to grow, developing efficient online solutions becomes ever more critical:

• Theoretical Implications: Improvements in algorithmic strategies push forward computational theory, influencing areas such as complexity theory and providing insights into problems that are solved incrementally.
• Practical Applications: Enhanced algorithms can lead to optimized systems in logistics, telecommunication, and adaptive systems, increasing efficiency and reducing operational costs.

Future Directions

Speculation on future developments in the field of online algorithms could focus on several fronts:

• Integration with Machine Learning: The emergence of adaptive and predictive models can revolutionize online algorithms, allowing systems to learn from previous data and improve decision-making processes dynamically.
• Scalability and Robustness: Addressing the challenges of scalability in distributed and high-throughput systems is imperative as data sources become more widespread and diverse.
• Cross-disciplinary Applications: Online algorithms might see increased application in areas such as finance for real-time trading systems or autonomous vehicles for real-time routing and navigation, highlighting their versatile utility.

In conclusion, while the specific content of "Online.pdf" is undisclosed, an understanding of the traditional elements involved in research on online algorithms provides a framework for appreciating the potential contributions and impact of research in this discipline. The continuous evolution of this topic suggests promising avenues for innovation and application across numerous technologically reliant fields.