Care Label Recognition

The paper introduces the problem of care label recognition and presents a method addressing it. A care label, also called a care tag, is a small piece of cloth or paper attached to a garment providing instructions for its maintenance and information about e.g. the material and size. The informationand instructions are written as symbols or plain text. Care label recognition is a challenging text and pictogram recognition problem - the often sewn text is small, looking as if printed using a non-standard font; the contrast of the text gradually fades, making OCR progressively more difficult. On the other hand, the information provided is typically redundant and thus it facilitates semi-supervised learning. The presented care label recognition method is based on the recently published End-to-End Method for Multi-LanguageScene Text, E2E-MLT, Busta et al. 2018, exploiting specific constraints, e.g. a care label vocabulary with multi-language equivalences. Experiments conducted on a newly-created dataset of 63 care label images show that even when exploiting problem-specific constraints, a state-of-the-art scene text detection and recognition method achieve precision and recall slightly above 0.6, confirming the challenging nature of the problem.

[1]  Xiaohui Zhao,et al.  CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor , 2019, ArXiv.

[2]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[4]  Wafa Khlif,et al.  ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[5]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[6]  Ernest Valveny,et al.  ICDAR 2015 competition on Robust Reading , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[7]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[8]  Jean-Yves Ramel,et al.  Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[9]  Shuchang Zhou,et al.  EAST: An Efficient and Accurate Scene Text Detector , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Martin Holecek,et al.  Line-items and table understanding in structured documents , 2019, ArXiv.

[11]  Shijian Lu,et al.  ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17) , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[12]  Jiri Matas,et al.  E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text , 2018, ACCV Workshops.

[13]  W. Marsden I and J , 2012 .

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.