Towards Automated Evaluation of Handwritten Assessments Automated evaluation of handwritten answers has been a challenging problem for scaling the education system for many years. Speeding up the evaluation remains as the major bottleneck for enhancing the throughput of instructors. This paper describes an effective method for automatically evaluating the short descriptive handwritten answers from the digitized images. Our goal is to evaluate a studentu0027s handwritten answer by assigning an evaluation score that is comparable to the humanassigned scores. Existing works in this domain mainly focused on evaluating handwritten essays with handcrafted, nonsemantic features. Our contribution is twofold: 1) we model this problem as a selfsupervised, featurebased classification problem, which can finetune itself for each question without any explicit supervision. 2) We introduce the usage of semantic analysis for autoevaluation in handwritten text space using the combination of Information Retrieval and Extraction (IRE) and, Natural Language Processing (NLP) methods to derive a set of useful features. We tested our method on three datasets created from various domains, using the help of students of different age groups. Experiments show that our method performs comparably to that of human evaluators.

A Scalable Handwritten Text Recognition System Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building stateoftheart models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTMbased models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.