Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images

• URL: http://arxiv.org/abs/2212.05525v3
• Date: Mon, 16 Oct 2023 16:11:58 GMT
• Title: Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images
• Authors: Hongkuan Zhang, Edward Whittaker, Ikuo Kitagishi
• Abstract summary: We propose a localization-free document-level OCR model for transcribing all the characters in a receipt image into an ordered sequence end-to-end. Specifically, we finetune the pretrained instance-level model TrOCR with randomly cropped image chunks. In our experiments, the model finetuned with our strategy achieved 64.4 F1-score and a 22.8% character error rate.
• Score: 0.07673339435080445
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Digitization of scanned receipts aims to extract text from receipt images and save it into structured documents. This is usually split into two sub-tasks: text localization and optical character recognition (OCR). Most existing OCR models only focus on the cropped text instance images, which require the bounding box information provided by a text region detection model. Introducing an additional detector to identify the text instance images in advance adds complexity, however instance-level OCR models have very low accuracy when processing the whole image for the document-level OCR, such as receipt images containing multiple text lines arranged in various layouts. To this end, we propose a localization-free document-level OCR model for transcribing all the characters in a receipt image into an ordered sequence end-to-end. Specifically, we finetune the pretrained instance-level model TrOCR with randomly cropped image chunks, and gradually increase the image chunk size to generalize the recognition ability from instance images to full-page images. In our experiments on the SROIE receipt OCR dataset, the model finetuned with our strategy achieved 64.4 F1-score and a 22.8% character error rate (CER), respectively, which outperforms the baseline results with 48.5 F1-score and 50.6% CER. The best model, which splits the full image into 15 equally sized chunks, gives 87.8 F1-score and 4.98% CER with minimal additional pre or post-processing of the output. Moreover, the characters in the generated document-level sequences are arranged in the reading order, which is practical for real-world applications.

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