RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts

We present a novel large vocabulary OCR system, which implements a confidence- and margin-based discriminative training approach for model adaptation of an HMM-based recognition system to handle multiple fonts, different handwriting styles, and their variations. Most current HMM approaches are HTK-based systems which are maximum likelihood (ML) trained and which try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer-specific data. Here, discriminative training based on the maximum mutual information (MMI) and minimum phone error (MPE) criteria are used instead. For model adaptation during decoding, an unsupervised confidence-based discriminative training within a two-pass decoding process is proposed. Additionally, we use neural network-based features extracted by a hierarchical multi-layer perceptron (MLP) network either in a hybrid MLP/HMM approach or to discriminatively retrain a Gaussian HMM system in a tandem approach. The proposed framework and methods are evaluated for closed-vocabulary isolated handwritten word recognition on the IFN/ENIT-database Arabic handwriting database, where the word error rate is decreased by more than 50 % relative to an ML trained baseline system. Preliminary results for large vocabulary Arabic machine-printed text recognition tasks are presented on a novel publicly available newspaper database.