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Reading comprehension is largely tested in schools using handwritten responses. The paper describes computational methods of scoring such responses using handwriting recognition and automatic essay scoring technologies. The goal is to assign to each handwritten response a score which is comparable to that of a human scorer even though machine handwriting recognition methods have high transcription error rates. The approaches are based on coupling methods of document image analysis and recognition together with those of automated essay scoring. Document image-level operations include: removal of pre-printed matter, segmentation of handwritten text lines and extraction of words. Handwriting recognition is based on a fusion of analytic and holistic methods together with contextual processing based on trigrams. The lexicons to recognize handwritten words are derived from the reading passage …