An Improved Arabic On-Line Characters Recognition System

Tlemsani, Redouane; Belbachir, Khadidja

doi:10.1109/acit.2018.8672673

Cited by 4 publications

(1 citation statement)

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“…Models in which both an image of the text along with pen tracking data were used to develop a Japanese handwriting recognition system [22]. Different language specific systems were implemented to apply recognition systems for different writing styles, such as Arabic [45] and Chinese [28].…”

Section: Positional-data Based Systemsmentioning

confidence: 99%

Towards an IMU-based Pen Online Handwriting Recognizer

Wehbi¹,

Hamann²,

Barth³

et al. 2021

Preprint

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Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss that allows the interpretation of raw sensor data into words without the need of sequence segmentation. We use a dataset of words collected using multiple sensor-enhanced pens and evaluate our model on distinct test sets of seen and unseen words achieving a character error rate of 17.97% and 17.08%, respectively, without the use of a dictionary or language model.

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Section: Positional-data Based Systemsmentioning

confidence: 99%