Premature　ventricular　contraction　(PVC)　is　one　of　the　most　common　arrhythmias　which　can　cause　palpitation,　cardiac　arrest,　and　other　symptoms　affecting　the　work　and　rest　activities　of　a　patient.　However,　patients　hardly　decipher　their　own　feelings　to　determine　the　severity　of　the　disease　thus,　requiring　a　professional　medical　diagnosis.　This　study　proposes　a　novel　method　based　on　image　processing　and　convolutional　neural　network　(CNN)　to　extract　electrocardiography　(ECG)　curves　from　scanned　ECG　images　derived　from　clinical　ECG　reports,　and　segment　and　classify　heartbeats　in　the　absence　of　a　digital　ECG　data.　The　ECG　curve　is　extracted　using　a　comprehensive　algorithm　that　combines　the　OTSU　algorithm　with　erosion　and　dilation.　This　algorithm　can　efficiently　and　accurately　separate　the　ECG　curve　from　the　ECG　background　grid.　The　performance　of　the　classification　model　was　evaluated　and　optimized　using　hundreds　of　clinical　ECG　data　collected　from　Fujian　Provincial　Hospital.　Additionally,　thousands　of　clinical　ECG　reports　were　scanned　to　digital　images　as　the　test　set　to　confirm　the　accuracy　of　the　algorithm　for　practical　application.　Results　showed　that　the　average　sensitivity,　specificity,　positive　predictive　value,　and　accuracy　of　the　proposed　model　on　the　MIT-BIH　dataset　were　95.47%,　97.72%,　98.75%,　and　98.25%,　respectively.　The　classification　average　sensitivity,　specificity,　positive　predictive　value,　and　accuracy　based　on　clinical　scanned　ECG　images　can　reach　to　97.24%,　81.6%,　83.8%,　and　89.33%,　respectively,　and　the　clinical　feasibility　is　high.　Overall,　the　proposed　method　can　extract　ECG　curves　from　scanned　ECG　images　efficiently　and　accurately.　Furthermore,　it　performs　well　on　heartbeat　classification　of　normal　(N)　and　ventricular　premature　heartbeat.