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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA)
Haeruddin
International University of Batam
Computer Science & IT Control & Systems Engineering

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AN END-TO-END CRNN AND CTC APPROACH FOR OFFLINE HANDWRITTEN CHINESE TEXT RECOGNITION Stefanus Eko Prasetyo; Jeffrey; Haeruddin
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4519

Abstract

Offline handwritten Chinese text recognition remains a challenging problem due to the large number of character classes, complex character structures, and high variability in writing styles. This paper proposes an end-to-end offline handwritten Chinese text recognition system based on a CNN–BiLSTM–CTC architecture. A convolutional neural network (CNN) is used to extract spatial features from handwritten text images, while a bidirectional long short-term memory (BiLSTM) network captures contextual dependencies between characters in both forward and backward directions. The Connectionist Temporal Classification (CTC) framework is applied to enable segmentation-free training and decoding of character sequences directly from input images. Experiments conducted on the CASIA-HWDB 2.2 offline handwritten Chinese text dataset demonstrate that the proposed approach achieves a Character Error Rate (CER) of 16,85%, corresponding to an accuracy of 83,15%, confirming the effectiveness of the CNN–BiLSTM–CTC framework for offline handwritten Chinese text recognition.
Co-Authors Jeffrey Stefanus Eko Prasetyo