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MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining | | Pengyuan Lyu
; Chengquan Zhang
; Shanshan Liu
; Meina Qiao
; Yangliu Xu
; Liang Wu
; Kun Yao
; Junyu Han
; Errui Ding
; Jingdong Wang
; | | Date: |
1 Jun 2022 | | Abstract: | In this paper, we present a model pretraining technique, named MaskOCR, for
text recognition. Our text recognition architecture is an encoder-decoder
transformer: the encoder extracts the patch-level representations, and the
decoder recognizes the text from the representations. Our approach pretrains
both the encoder and the decoder in a sequential manner. (i) We pretrain the
encoder in a self-supervised manner over a large set of unlabeled real text
images. We adopt the masked image modeling approach, which shows the
effectiveness for general images, expecting that the representations take on
semantics. (ii) We pretrain the decoder over a large set of synthesized text
images in a supervised manner and enhance the language modeling capability of
the decoder by randomly masking some text image patches occupied by characters
input to the encoder and accordingly the representations input to the decoder.
Experiments show that the proposed MaskOCR approach achieves superior results
on the benchmark datasets, including Chinese and English text images. | | Source: | arXiv, 2206.00311 | | Services: | Forum | Review | PDF | Favorites | |
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