General Detection-based Text Line Recognition

Abstract: We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR), with Latin, Chinese, or ciphered characters. Detection-based approaches have until now been largely discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with sufficiently diverse data enables learning reasonable character localization for any script; (ii) modern transformer-based detectors can jointly detect a large number of instances, and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet. Our approach, dubbed DTLR, builds on a completely different paradigm than state-of-the-art HTR methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, we demonstrate good performance on a large range of scripts, usually tackled with specialized approaches. In particular, we improve state-of-the-art performances for Chinese script recognition on the CASIA v2 dataset, and for cipher recognition on the Borg and Copiale datasets. Our code and models are available at https://github.com/raphael-baena/DTLR.

• The paper introduces DTLR, a novel detection-based framework for text line recognition that identifies characters in parallel using transformer models, diverging from traditional autoregressive methods.
• DTLR leverages synthetic pre-training for robust character localization across scripts and allows fine-tuning with only line-level annotations, enhancing flexibility for diverse writing systems.
• The DTLR method achieves state-of-the-art performance on challenging datasets for Chinese handwriting and historical ciphers, demonstrating the practical efficacy and potential of detection-based approaches.

General Detection-based Text Line Recognition: A Comprehensive Overview

The paper "General Detection-based Text Line Recognition" by Raphael Baena et al. explores a novel approach for text line recognition that emphasizes a detection-based methodology. Unlike conventional handwritten text recognition (HTR) strategies that rely on autoregressive decoding or recurrent models, this study leverages detection paradigms to recognize text lines concurrently. The significance of this work extends across various scripts, including Latin, Chinese, and ciphers, broadening the applicability of detection-based methods in scenarios typically dominated by segmentation-free models.

The authors introduce DTLR (Detection-based Text Line Recognition) as their proposed framework, which fundamentally diverges from prevailing autoregressive techniques by detecting characters in parallel through modern transformer-based detectors. Several core insights underlie this approach:

1. Synthetic Pre-training: A key observation from the paper is that pre-training with robust and diverse synthetic datasets facilitates the learning of character localization across different scripts. This allows the model to generalize from synthetic to real-world data effectively.
2. Transformer-based Detection: The use of transformers is central to DTLR's architecture, enabling the detection of multiple character instances in parallel. The study highlights that these detectors benefit from detection consistency, particularly when trained with a masking strategy that enhances inter-character relationships.
3. Line-level Annotation Fine-tuning: DTLR demonstrates the capacity to fine-tune pre-trained models using only line-level annotations, even when dealing with scripts not encountered during pre-training. This flexibility is vital for adaption across diverse alphabets and writing systems.

This research presents a paradigm shift in text line recognition by revisiting character detection with contemporary tools and pre-training strategies. The method's capacity to outperform state-of-the-art models, particularly in difficult script recognition tasks such as Chinese handwriting on the CASIA v2 dataset and cipher recognition across Borg and Copiale datasets, underscores its versatility and potential impact.

Numerical Results and Comparative Analysis

DTLR has been empirically validated on a wide range of benchmarks, yielding significant improvements over prior art. For instance, in the Chinese script recognition task on the CASIA v2 dataset, the proposed method achieved an Accurate Rate (AR) and Correct Rate (CR) surpassing previous records. Similarly, in cipher recognition tasks, it outperformed existing models, reducing the Symbol Error Rates (SER) significantly. These results illustrate the practical efficacy of DTLR, marking a substantial contribution to the field and demonstrating the promise of detection-based approaches beyond scene text recognition.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the DTLR framework provides an alternative to existing text recognition systems, promising enhancements in computational efficiency and interpretability due to its parallel processing capabilities. Theoretically, it challenges the prevailing narrative that implicit segmentation methods are inherently superior for handwritten text recognition, opening avenues for further exploration in detection-based paradigms.

Furthermore, the integration of transformer-based models in character detection, supported by synthetic pre-training, sets a precedent for future developments in artificial intelligence, particularly in the recognition of complex scripts and underrepresented written languages. This research advocates for a broader acknowledgment of detection strategies and encourages the community to consider their applications in multifarious text recognition contexts.

In conclusion, the paper by Baena et al. provides a thorough and compelling argument for the revival and modernization of detection-based text line recognition. Its blend of theoretical novelty and practical success invites further research to capitalize on the untapped potential of detection-oriented methods in the ever-expanding landscape of text recognition technologies.