Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper is focused on OCR for Chinese characters, with a focus on rarely used characters. Given the large and diverse set of characters in the Chinese language, the authors propose using a face detection model for the OCR task, which gives promising results.
strengths_and_weaknesses: Strengths:

-	The paper is well written with a clearly stated goal and promising results. 

-	The model was explained with sufficient detail for this format.

Weaknesses:

-	No baseline model is included. While the accuracy is seemingly respectable, it is not clear from the paper how a traditional OCR approach would have fared. Given that the central claim of the paper is that a face detection model is a better fit than an OCR model, this is necessary.

-	I do not fully understand was comprises the train and test set. The abstract mentions 74000 characters, but the training set is only comprised of 5000. Were these randomly sampled? Is there any differentiation between rare or commonly used characters? Table 1 indicates 5000 characters in the test set, are these different than the training set?

suggested_changes: -	Provide a baseline model as a point of comparison.

-	Clearly indicate what comprises the training and test sets.

-	Given the focus of the paper is rarely used characters, a brief discussion that focuses on results for those characters specifically would be a worthwhile inclusion.

-	Further proofreading would be ideal. While the paper is readable in its current state, the English grammar is poor in several parts.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The study proposes using MobileFaceNetV3 for Chinese character recognition, achieving over 90% accuracy on various font datasets. 
strengths_and_weaknesses: ## Strengths:

1. The paper uses  MobileFaceNetV3, a tiny and light network that outputs a 64-dimension feature vector, which is effective in recognizing Chinese characters. It is a perfect use case for transfer learning.
2. The establishment of a website that makes it convenient for cultural researchers to validate Chinese characters.

##  Weakness:

The limitations of the research include the lack of a hand-writing character dataset, which limits the ability of the model to recognize hand-written characters. Additionally, while the recognition accuracy exceeded 90% on various datasets, the model may have difficulty with more complex characters that become indistinguishable from other similar ones after blurring. Also, the use of computer-generated data may not fully represent the variability of real-world handwriting, which could affect the generalizability of the model.


suggested_changes: 1. Including real-world handwriting in the training to solve it for real handwriting would be a great next step.
2. Please consider evaluating the study along with the SSD-Mobilenet implementation.
review_title: null

Meta-review: metareview: The paper is interesting and potentially valuable in some ways, but are missing some important details according to both reviewers. The strengths and weaknesses are given below

Strengths:
- Novel approach to OCR
- Dataset creation

Weaknesses:
- Lack of relevant research citation, as mentioned (SSD Mobilenet)
- Lack of baseline model
- Unclear experimentation details (train/test split)

At the time of this meta-review, the website is inaccessible
confidence: 3: The area chair is somewhat confident
Decision: Invite to present