Platypus: A Generalized Specialist Model for Reading Text in Various
  Forms
Reading text from images (either natural scenes or documents) has been a
long-standing research topic for decades, due to the high technical challenge
and wide application range. Previously, individual specialist models are
developed to tackle the sub-tasks of text reading (e.g., scene text
recognition, handwritten text recognition and mathematical expression
recognition). However, such specialist models usually cannot effectively
generalize across different sub-tasks. Recently, generalist models (such as
GPT-4V), trained on tremendous data in a unified way, have shown enormous
potential in reading text in various scenarios, but with the drawbacks of
limited accuracy and low efficiency. In this work, we propose Platypus, a
generalized specialist model for text reading. Specifically, Platypus combines
the best of both worlds: being able to recognize text of various forms with a
single unified architecture, while achieving excellent accuracy and high
efficiency. To better exploit the advantage of Platypus, we also construct a
text reading dataset (called Worms), the images of which are curated from
previous datasets and partially re-labeled. Experiments on standard benchmarks
demonstrate the effectiveness and superiority of the proposed Platypus model.
Model and data will be made publicly available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/Platypus.
66ce83b2da5d6bbe4d002b25
Peng Wang
66ce83b2da5d6bbe4d002b26
Zhaohai Li
66ce83b2da5d6bbe4d002b27
Jun Tang
66ce83b2da5d6bbe4d002b28
Humen Zhong
66ce83b2da5d6bbe4d002b29
Fei Huang
admin_assigned
2024-08-28T09:34:52.501Z
635b8b6a37c6a2c12e2cce00
/avatars/229fb72180529141515d1df797b33709.svg
Fei Huang
user
hzhwcmhf
66ce83b2da5d6bbe4d002b2a
Zhibo Yang
admin_assigned
2024-08-28T09:34:13.970Z
66051de91b8220d71becb448
/avatars/f2b507f5821f8baa90b60f62ae4de389.svg
杨智博
user
zhiboyang
66ce83b2da5d6bbe4d002b2b
Cong Yao
admin_assigned
2024-08-28T09:34:03.965Z
6638f898c168eec02354abeb
/avatars/0a0b7a1b056463fb4ccf6bfe6456a7d1.svg
Cong Yao
user
bridgetop3young
Platypus: A Generalized Specialist Model for Reading Text in Various
  Forms
Reading text from images (either natural scenes or documents) has been a
long-standing research topic for decades, due to the high technical challenge
and wide application range. Previously, individual specialist models are
developed to tackle the sub-tasks of text reading (e.g., scene text
recognition, handwritten text recognition and mathematical expression
recognition). However, such specialist models usually cannot effectively
generalize across different sub-tasks. Recently, generalist models (such as
GPT-4V), trained on tremendous data in a unified way, have shown enormous
potential in reading text in various scenarios, but with the drawbacks of
limited accuracy and low efficiency. In this work, we propose Platypus, a
generalized specialist model for text reading. Specifically, Platypus combines
the best of both worlds: being able to recognize text of various forms with a
single unified architecture, while achieving excellent accuracy and high
efficiency. To better exploit the advantage of Platypus, we also construct a
text reading dataset (called Worms), the images of which are curated from
previous datasets and partially re-labeled. Experiments on standard benchmarks
demonstrate the effectiveness and superiority of the proposed Platypus model.
Model and data will be made publicly available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/Platypus.
Platypus is a unified text reading model that combines the versatility of generalist models with the high accuracy and efficiency of specialist models, demonstrated on various benchmarks.
scene text recognition handwritten text recognition mathematical expression recognition GPT-4V generalized specialist model Worms text reading dataset