Jens Forster

Moment-Based Image Normalization for Handwritten Text Recognition

In this paper, we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text. Image moments provide robust estimates for text characteristics such as size and position of words within an image. For handwriting recognition the normalization procedure is applied to image slices independently. Additionally, a novel moment-based algorithm for line-thickness normalization is presented. The proposed normalization methods are evaluated on the RIMES database of French handwriting and the IAM database of English handwriting. For RIMES we achieve an improvement from 16.7% word error rate to 13.4% and for IAM from 46.6% to 37.3%.

Continuous Sign Language Recognition: Towards Large Vocabulary Statistical Recognition Systems Handling Multiple Signers

Abstract This work presents a statistical recognition approach performing large vocabulary continuous sign language recognition across different signers. Automatic sign language recognition is currently evolving from artificial lab-generated data to ‘real-life’ data. To the best of our knowledge, this is the first time system design on a large data set with true focus on real-life applicability is thoroughly presented. Our contributions are in five areas, namely tracking, features, signer dependency, visual modelling and language modelling. We experimentally show the importance of tracking for sign language recognition with respect to the hands and facial landmarks. We further contribute by explicitly enumerating the impact of multimodal sign language features describing hand shape, hand position and movement, inter-hand-relation and detailed facial parameters, as well as temporal derivatives. In terms of visual modelling we evaluate non-gesture-models, length modelling and universal transition models. Signer-dependency is tackled with CMLLR adaptation and we further improve the recognition by employing class language models. We evaluate on two publicly available large vocabulary databases representing lab-data (SIGNUM database: 25 signers, 455 sign vocabulary, 19k sentences) and unconstrained ‘real-life’ sign language (RWTH-PHOENIX-Weather database: 9 signers, 1081 sign vocabulary, 7k sentences) and achieve up to 10.0%/16.4% and respectively up to 34.3%/53.0% word error rate for single signer/multi-signer setups. Finally, this work aims at providing a starting point to newcomers into the field.

Efficient approximations to model-based joint tracking and recognition of continuous sign language

We propose several tracking adaptation approaches to recover from early tracking errors in sign language recognition by optimizing the obtained tracking paths w.r.t. to the hypothesized word sequences of an automatic sign language recognition system. Hand or head tracking is usually only optimized according to a tracking criterion. As a consequence, methods which depend on accurate detection and tracking of body parts lead to recognition errors in gesture and sign language processing. We analyze an integrated tracking and recognition approach addressing these problems and propose approximation approaches over multiple hand hypotheses to ease the time complexity of the integrated approach. Most state-of-the-art systems consider tracking as a preprocessing feature extraction part. Experiments on a publicly available benchmark database show that the proposed methods strongly improve the recognition accuracy of the system.

Tracking benchmark databases for video-based sign language recognition

A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented.

Analysis of Preprocessing Techniques for Latin Handwriting Recognition

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.

Modality Combination Techniques for Continuous Sign Language Recognition

Sign languages comprise parallel aspects and use several modalities to form a sign but so far it is not clear how to best combine these modalities in the context of statistical sign language recognition. We investigate early combination of features, late fusion of decisions, as well as synchronous combination on the hidden Markov model state level, and asynchronous combination on the gloss level. This is done for five modalities on two publicly available benchmark databases consisting of challenging real-life data and less complex lab-data, the state-of-the-art typically focusses on. Using modality combination, the best published word error rate on the SIGNUM database (lab-data) is improved from 11.9% to 10.7% and from 55% to 41.9% on the RWTH-PHOENIX database (challenging real-life data).