Journal
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Volume -, Issue -, Pages 7737-7742Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICPR48806.2021.9412198
Keywords
Handwritten Digit String; Segmentation; Recognition; U-Net
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Funding
- PURSE-II
- UPE-II, Jadavpur University
- DST, Govt. of India [EMR/2016/007213]
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This study successfully tackles the issue of handwritten digit string recognition through deep learning techniques to isolate digits in the string and recognize them using a ResNet model.
Recognition of isolated handwritten digits is a well-studied research problem and several models show high recognition accuracy on different standard datasets. But the same is not true while we consider recognition of handwritten digit strings although it has many real-life applications like bank cheque processing, postal code recognition, and numeric field understanding from filled-in form images. The problem becomes more difficult when digits in the string are not neatly written which is commonly seen in freestyle handwriting. The performance of any such model primarily suffers due to the presence of touching digits in the string. To handle these issues, in the present work, we first use a deep autoencoder based segmentation technique for isolating the digits from a handwritten digit string, and then we pass the isolated digits to a Residual Network (ResNet) based recognition model to obtain the machine-encoded digit string. The proposed model has been evaluated on the Computer Vision Lab (CVL) Handwritten Digit Strings (HDS) database, used in HDSRC 2013 competition on handwritten digit string recognition, and a competent result with respect to state-of-the-art techniques has been achieved.
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