DLoRA-TrOCR: Mixed Text Mode Optical Character Recognition Based On Transformer

• URL: http://arxiv.org/abs/2404.12734v3
• Date: Tue, 23 Apr 2024 05:36:31 GMT
• Title: DLoRA-TrOCR: Mixed Text Mode Optical Character Recognition Based On Transformer
• Authors: Da Chang, Yu Li,
• Abstract summary: Multi- fonts, mixed scenes and complex layouts seriously affect the recognition accuracy of traditional OCR models. We propose a parameter-efficient mixed text recognition method based on pre-trained OCR Transformer, namely DLoRA-TrOCR.
• Score: 12.966765239586994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the continuous development of Optical Character Recognition (OCR) and the expansion of application fields, text recognition in complex scenes has become a key challenge. Factors such as multiple fonts, mixed scenes and complex layouts seriously affect the recognition accuracy of traditional OCR models. Although OCR models based on deep learning have performed well in specific fields or similar datasets in recent years, the generalization ability and robustness of the model are still a big challenge when facing complex environments with multiple scenes. Furthermore, training an OCR model from scratch or fine-tuning all parameters is very demanding on computing resources and inference time, which limits the flexibility of its application. This study focuses on a fundamental aspect of mixed text recognition in response to the challenges mentioned above, which involves effectively fine-tuning the pre-trained basic OCR model to demonstrate exceptional performance across various downstream tasks. To this end, we propose a parameter-efficient mixed text recognition method based on pre-trained OCR Transformer, namely DLoRA-TrOCR. This method embeds DoRA into the image encoder and LoRA into the internal structure of the text decoder, enabling efficient parameter fine-tuning for downstream tasks. Experiments show that compared to similar parameter adjustment methods, our model DLoRA-TrOCR has the smallest number of parameters and performs better. It can achieve state-of-the-art performance on complex scene datasets involving simultaneous recognition of mixed handwritten, printed and street view texts.

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