The detection-based approach, while innovative, does have potential limitations compared to autoregressive models, particularly in the following areas:
Long-Range Dependencies: Detection-based models typically treat character detection as an independent task, which may hinder their ability to capture long-range dependencies within a text line. Autoregressive models, on the other hand, inherently consider the sequence of characters, allowing them to leverage contextual information from previously predicted characters to inform subsequent predictions. This is particularly important in languages with complex grammatical structures or when dealing with phrases where the meaning is dependent on the arrangement of words.
Handling Rare Characters: Detection-based approaches may struggle with rare characters or symbols, especially if they have not been adequately represented in the training data. Autoregressive models can benefit from their sequential nature, allowing them to utilize language models that have been trained on large corpora to predict rare characters based on context. In contrast, detection-based models may lack the necessary contextual cues to accurately identify these characters, leading to higher error rates.
Character Localization: While the detection-based approach excels in character localization, it may not always provide the same level of precision in character recognition as autoregressive models, which can refine their predictions based on the entire sequence. This could result in misclassifications, particularly in cases where characters are visually similar or when they appear in challenging contexts.
Complexity of Training: The training process for detection-based models can be more complex, requiring careful tuning of hyperparameters and loss functions to ensure effective character localization and classification. In contrast, autoregressive models often benefit from established training paradigms, such as Connectionist Temporal Classification (CTC), which can simplify the training process.
Overall, while the detection-based approach offers significant advantages in terms of interpretability and parallel processing, it may face challenges in capturing long-range dependencies and accurately recognizing rare characters compared to autoregressive models.