EndtoEnd Measure for Text Recognition Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist wellestablished measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The Fmeasure on word level is a wellknown methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the endtoend performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an endtoend text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited xe2x80x93 in its original form xe2x80x93 to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore overand undersegmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.

Towards Document Image Quality Assessment: A Text Line Based Framework and a Synthetic Text Line Image Dataset Since the low quality of document images will greatly undermine the chances of success in automatic text recognition and analysis, it is necessary to assess the quality of document images uploaded in online business process, so as to reject those images of low quality. In this paper, we attempt to achieve document image quality assessment and our contributions are twofold. Firstly, since document image quality assessment is more interested in text, we propose a text line based framework to estimate document image quality, which is composed of three stages: text line detection, text line quality prediction, and overall quality assessment. Text line detection aims to find potential text lines with a detector. In the text line quality prediction stage, the quality score is computed for each text line with a CNNbased prediction model. The overall quality of document images is finally assessed with the ensemble of all text line quality. Secondly, to train the prediction model, a largescale dataset, comprising 52,094 text line images, is synthesized with diverse attributes. For each text line image, a quality label is computed with a piecewise function. To demonstrate the effectiveness of the proposed framework, comprehensive experiments are evaluated on two popular document image quality assessment benchmarks. Our framework significantly outperforms the stateoftheart methods by large margins on the large and complicated dataset.