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 is
inefficient, 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 Transformer-based 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 rates (CER) on the word-level and character-level metrics, 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.

本研究提出了一个基于 Transformer 的 OCR 模型，用于提取收据图像中的字符序列，无需定位文本。该模型能够在不需要额外处理的情况下，将整个收据图像中的字符识别准确，并按阅读顺序排列。