Abstract: In this paper, we propose a new network architecture for Chinese typography transformation based on deep learning. The architecture consists of two sub-networks: (1)a fully convolutional network(FCN) aiming at transferring specified typography style to another in condition of preserving structure information; (2)an adversarial network aiming at generating more realistic strokes in some details. Unlike models proposed before 2012 relying on the complex segmentation of Chinese components or strokes, our model treats every Chinese character as an inseparable image, so pre-processing or post-preprocessing are abandoned. Besides, our model adopts end-to-end training without pre-trained used in other deep models. The experiments demonstrates that our model can synthesize realistic-looking target typography from any source typography both on printed style and handwriting style.
Assessing Accessibility and Operational Challenges in arXiv's Current System
The unavailability of a PDF for the paper identified as (Chang et al., 2017)v2 brings to light various operational aspects and challenges associated with the arXiv platform. Due to the absence of direct content, this analysis focuses on the implications of such gaps in academic dissemination, impacting researchers' ability to access and evaluate scientific works efficiently.
First, it is pertinent to recognize that arXiv serves as a critical resource for the dissemination of preliminary scientific papers particularly in fields such as computer science, physics, and mathematics. As such, issues in accessibility, such as missing PDFs or incomplete metadata, impede the dual goals of this repository: fostering open access to scientific preprints and facilitating collaboration among researchers. This specific instance highlights a broader challenge concerning arXiv's reliance on author-provided resources to generate comprehensive and accessible paper formats.
The operational shortcoming demonstrated by the absence of a PDF may implicate several areas of future improvement for the platform:
Enhancement of Submission Protocols: Development of more robust submission protocols could ensure that authors consistently provide necessary materials in correct formats, thereby reducing the incidence of incomplete entries.
Automated Verification Systems: Implementing automated processes to verify the completeness and coherence of submissions before public listing might serve as an effective quality control measure.
User Feedback Mechanisms: Providing enhanced user feedback options could help identify systemic issues expediently, allowing for real-time improvements and maintaining the repository's functional integrity.
The implications of such operational challenges reach beyond mere accessibility. From a theoretical perspective, they touch upon the broader discourse of open-access dissemination of scientific knowledge. Inefficiencies in access mechanisms might inadvertently bias visibility towards more comprehensively documented submissions, irrespective of content quality.
On a practical level, these issues necessitate a reevaluation of the support structures that underpin repositories like arXiv. There is potential for explorative development of decentralized, peer-verified platforms that leverage blockchain or similar distributed ledger technologies to ensure immutable and universally accessible scientific records.
In essence, while the lack of a source to generate a PDF for (Chang et al., 2017)v2 seems a straightforward operational glitch, it underscores significant challenges in the digital dissemination of academic research. As the landscape of academic publication continues to evolve, fostering a robust infrastructure that guarantees both the availability and quality of scientific submissions remains imperative. This incident serves as a catalyst for ongoing dialogue concerning the optimization of scholarly communication systems, critical in supporting the dynamic needs of the research community.