added keras support

app/Http/Controllers/Api/OCRController.php

@@ -175,6 +175,10 @@ class OCRController extends ApiController
             $data['ocr']['pixelate'] = $result;
             imagedestroy($imgcreate);
 
+            // keras
+            $cmd = "python3 scripts/keras.py " . base64_encode(file_get_contents($tmpfname));
+            $command = escapeshellcmd($cmd); #no special characters it will work
+            $data['ocr']['keras'] = shell_exec($command);
 
             unlink($tmpfname);
             return $this->respondJson($data);
@@ -203,28 +207,4 @@ class OCRController extends ApiController
 
     // // Do something with the text from Tesseract
     // echo $text;
-
-
-    // function is_ani($filename) {
-    //     if(!($fh = @fopen($filename, 'rb')))
-    //       return false;
-    //     $count = 0;
-    //     //an animated gif contains multiple "frames", with each frame having a
-    //     //header made up of:
-    //     // * a static 4-byte sequence (\x00\x21\xF9\x04)
-    //     // * 4 variable bytes
-    //     // * a static 2-byte sequence (\x00\x2C) (some variants may use \x00\x21 ?)
-
-    //     // We read through the file til we reach the end of the file, or we've found
-    //     // at least 2 frame headers
-    //     $chunk = false;
-    //     while(!feof($fh) && $count < 2) {
-    //       //add the last 20 characters from the previous string, to make sure the searched pattern is not split.
-    //       $chunk = ($chunk ? substr($chunk, -20) : "") . fread($fh, 1024 * 100); //read 100kb at a time
-    //       $count += preg_match_all('#\x00\x21\xF9\x04.{4}\x00(\x2C|\x21)#s', $chunk, $matches);
-    //     }
-
-    //     fclose($fh);
-    //     return $count > 1;
-    //   }
 }

scripts/keras.py

@@ -0,0 +1,17 @@
+import io
+import sys
+import base64
+import numpy as np
+import keras_ocr
+
+if len(sys.argv) > 1:
+    # Decode the base64-encoded image
+    img = base64.b64decode(sys.argv[1])
+    img = np.array(bytearray(img), dtype=np.uint8)
+
+    # Use Keras-OCR to recognize text in the image
+    pipeline = keras_ocr.pipeline.Pipeline()
+    prediction = pipeline.recognize([img])
+
+    # Return the recognized text
+    print prediction[0][0]['text']