DTrOCR: Decoder-only Transformer for Optical Character Recognition

• URL: http://arxiv.org/abs/2308.15996v1
• Date: Wed, 30 Aug 2023 12:37:03 GMT
• Title: DTrOCR: Decoder-only Transformer for Optical Character Recognition
• Authors: Masato Fujitake
• Abstract summary: We propose a simpler and more effective method for text recognition, known as the Decoder-only Transformer for Optical Character Recognition (DTrOCR) This method uses a decoder-only Transformer to take advantage of a generative language model that is pre-trained on a large corpus. Our experiments demonstrated that DTrOCR outperforms current state-of-the-art methods by a large margin in the recognition of printed, handwritten, and scene text in both English and Chinese.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Typical text recognition methods rely on an encoder-decoder structure, in which the encoder extracts features from an image, and the decoder produces recognized text from these features. In this study, we propose a simpler and more effective method for text recognition, known as the Decoder-only Transformer for Optical Character Recognition (DTrOCR). This method uses a decoder-only Transformer to take advantage of a generative language model that is pre-trained on a large corpus. We examined whether a generative language model that has been successful in natural language processing can also be effective for text recognition in computer vision. Our experiments demonstrated that DTrOCR outperforms current state-of-the-art methods by a large margin in the recognition of printed, handwritten, and scene text in both English and Chinese.

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