  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.
