Attention-based deep learning model for Arabic handwritten text recognition

Abstract

This work proposes a segmentation-free approach to Arabic Handwritten Text Recognition (AHTR): an attention-based Convolutional Neural Network - Recurrent Neural Network - Connectionist Temporal Classification (CNN-RNN-CTC) deep learning architecture. The model receives as input an image and provides, through a CNN, a sequence of essential features, which are transferred to an Attention-based Bidirectional Long Short-Term Memory Network (BLSTM). The BLSTM gives features sequence in order, and the attention mechanism allows the selection of relevant information from the features sequences. The selected information is then fed to the CTC, enabling the loss calculation and the transcription prediction. The contribution lies in extending the CNN by dropout layers, batch normalization, and dropout regularization parameters to prevent over-fitting. The output of the RNN block is passed through an attention mechanism to utilize the most relevant parts of the input sequence in a flexible manner. This solution enhances previous methods by improving the CNN speed and performance and controlling over model over-fitting. The proposed system achieves the best accuracy of 97.1% for the IFN-ENIT Arabic script database, which competes with the current state-of-the-art. It was also tested for the modern English handwriting of the IAM database, and the Character Error Rate of 2.9% is attained, which confirms the model's script independence.

Handwritten character recognition using optimization based skewed line segmentation method and multi-class support vector machine

“…Segmenting text lines from document images presents one of the biggest hurdles in document image analysis. Text block segmentation and text line segmentation give essential information [51] for the tasks of character and text string recognition. Images that are free of noise and skew are submitted to the system to be processed further.…”

Classification and Localization of Arabic Handwritten Text in KHATT Dataset Based on Faster R-CNN

“…Recent advancements in deep learning technology have significantly Influenced the field of Arabic text recognition, leading to many solutions based on deep learning approaches [7]. The progress made in deep learning (DL) has resulted in remarkable advancements, particularly in computer vision.…”

“…CNN deals with classification, recognition, multi-object detection, item localization, and handwriting recognition, and some popular CNN architectures include ResNet, VGG, AlexNet, and GoogleNet. Recurrent neural networks (RNNs) like Long Short-Term Memory (LSTM) networks, restricted Boltzmann machines (RBM), Deep Belief Networks (DBN), and Hidden Markov Model (HMM) are among the various deep learning methods extensively employed in handwriting recognition tasks [3], [7]- [11]. Despite their efficiency, these methods suffer from many drawbacks.…”