EasyOCR inconsistent in recognizing letter "e" when by itself

I have a function in Python which reads text in Portuguese with EasyOCR. For some reason it doesn't always recognize the "e" between bigger words, which is a common connector word in this language.

Is there any way in which I could force EasyOCR to recognize the "e" as part of the phrase?

def getOCRResults(imgFP)->list[tuple[tuple[int,int,int,int]|None,str|None,float|None]]:

    reader = easyocr.Reader(['pt'], gpu=False)
    result = reader.readtext(imgFP, width_ths=0.7, add_margin=0.2, height_ths=0.8)
    return result


def draw_bounding_boxes(image, detections, threshold=0.25):
    for bbox, text, score in detections:
        if score > threshold:
            cv2.rectangle(image, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (0, 255, 0), 1)
            cv2.putText(image, text, tuple(map(int, bbox[0])), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 0, 0), 1)

OCRResults = getOCRResults(image_path)
img = cv2.imread(image_path)
draw_bounding_boxes(img,OCRResults,0.25)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGBA))
plt.show()

Part of image where letter "e" not recognized as text when alone.

Another area in the same image where it recognized the "e" as part of text in some of the options (2º and 4º). The 1º e 3º options in the combobox also show that the "e" hasn't been recognized.

Try reducing the margin, and the low_text and text_threshold arguments.

reader.readtext(
    imgFP, 
    width_ths=0.7, 
    add_margin=0.1, 
    height_ths=0.8, 
    low_text=0.3,       # default 0.4
    text_threshold=0.6  # default 0.7
)

Your margin is larger than you need it to be, which might be occluding other text boxes. A lower low_text parameter will include more low confidence regions when deciding if a region contains text during detection. A lower text_threshold parameter will include lower confidence guesses at what is in the text. Adjusting these parameters can help you balance between detecting more text regions (including low-confidence ones) and reducing false positives by being more stringent.